Browse Topic: Sensors and actuators

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Design and Optimization of an LQR-Based Multivariable Control System for an RBCC Engine2026-99-063211/6/2026
To achieve the key control objectives of thrust regulation and inlet protection for a rocket-based combined cycle (RBCC) engine under wide flight envelope, high-speed operating conditions, this study is based on a nonlinear component-level RBCC engine model developed in MATLAB/Simulink. A state space model is established using a least squares system identification method. By incorporating actuator dynamics, an augmented system model is formulated, and a Differential Evolution (DE) optimized Linear Quadratic Regulator (LQR) multivariable closed-loop control strategy is proposed. Simulation results show that the proposed strategy significantly improves both response speed and disturbance-rejection capability. Compared with the conventional LQR controller, the maximum oscillation of the inlet stability margin is reduced from 11% to 4.4%, and the thrust settling time is shortened by 83%. These results verify the feasibility of the proposed control method under complex operating conditions.
Zheng, Runtong, Han, Chongpeng, Xie, Zhenwei
Covariance-based Filtering and Smoothing Estimation under a Round-Robin Protocol2026-99-062711/6/2026
This paper focuses on recursive estimation for stochastic systems under the round-robin (RR) protocol. In recursive estimation, it is assumed that the state evolution process of the signal is unknown, and the relevant estimation research is only carried out relying on the mean and covariance functions of the process. To effectively avoid the occurrence of transmission channel conflicts and ensure the fairness of data allocation during transmission, this paper chooses to adopt the RR protocol to schedule and manage data communication. Specifically, this scheduling rule stipulates that only one sensor is allowed to acquire the network communication opportunity within a fixed time period, and the entire scheduling process is executed cyclically in a preset fixed order. Based on the observable information available in the system, this paper further designs and proposes a recursive estimation algorithm by means of the innovation method. Finally, a numerical simulation experiment is given to verify the effectiveness and feasibility of the proposed estimation algorithm.
Lin, Xiao, Li, Jiaxing, Du, Junhua, Wu, Meiyun
Compensation Strategy-Based Gaussian Approximation Fusion Filtering for Multi-Sensor Nonlinear Systems Subject to Markov DoS Attacks2026-99-061411/6/2026
The research concentrates on the Gaussian approximation fusion filtering (GAFF) issue for multi-sensor nonlinear systems under Markov denial of service (DoS) attacks. This paper aims to propose a fusion filtering algorithm for the addressed systems to cope with DoS attacks within the Gaussian approximation filtering framework, where the attacks follow the Markov property. In particular, a prediction compensation strategy is adopted to reduce the impact caused by the attacks as much as possible. In light of the available measurements, the Gaussian approximation local filtering unified framework is given, where the Gaussian weighted integrals are addressed by means of the cubature Kalman method. By using the sequential inverse covariance intersection fusion criterion, the GAFF algorithm is obtained to improve estimation accuracy. Finally, a simulation experiment with comparison tests is introduced to validate the effectiveness of the proposed GAFF method.
Luo, Ruonan, Hu, Jun, Jia, Chaoqing, Dao, Wen
Onboard AI-Driven Autonomous Tasking for Rapid Wildfire Response with a LEO Wide-Swath Observation Satellite2026-99-060911/6/2026
Low Earth Orbit (LEO) remote sensing satellites exhibit significant limitations in emergency response operations, primarily due to the constraints of orbital mechanics and a traditional reliance on ground-involved, open-loop operational models. Current response latencies often span several hours, rendering acquired data less effective for disasters with rapid temporal dynamics. We propose a new intra-pass autonomous reaction application mode to solve this key problem in emergency response. We build the mode on an agile satellite platform operating in an 800 km sun-synchronous orbit. We equip the platform with two core sensors through an innovative design. The two sensors are an optical camera with 0.5-meter resolution and a forward-looking infrared camera. The infrared camera has a 600-kilometer swath and a 10-meter nadir resolution. We take onboard artificial intelligence technologies as the research core. The technologies support real-time and autonomous analysis of forward-looking infrared image data. This analytical capability facilitates the real-time detection and autonomous confirmation of stochastic events, such as wildfires, by leveraging onboard artificial intelligence (AI) to perform real-time analysis of IR imagery. This subsequently triggers immediate autonomous re-tasking for high-resolution follow-up observation within the same orbital pass. We establish an optimization model for the satellite system. The model is used to solve the key balance problem between the sensor forward-looking angle, achievable reaction time, and actual detection resolution. We conduct a detailed engineering time budget analysis for the whole satellite operation system. Analysis results show that a specific forward-looking angle must be set for the infrared camera to obtain a 70-second autonomous response window of the satellite. The 70second window contains 30 seconds for AI cognitive analysis, a maximum of 33.6 seconds for platform attitude maneuvering, and 10 seconds for platform stability adjustment. The minimum forward-looking angle required for the infrared camera is about 33.6 degrees. The ground sampling distance of the infrared camera will decrease to 12.2 meters at this angle. The resulting 12.2-meter ground sampling distance (GSD) remains sufficient to meet the practical requirements of early-stage detection. The proposed Intra-Pass Autonomous Reaction (IPAR) mode reduces emergency response times from hours to seconds, significantly enhancing the proactive observation capabilities of LEO platforms.
Qiu, Hu, Zhao, Xinwei, Liu, Dongfusheng, Wang, Honghai
Research on Intelligent Switching and Control Strategy of Air-Sea Amphibious WIG Craft Based on Multi-Sensor Fusion2026-99-061911/6/2026
Aiming at the core problems of abrupt changes in dynamic characteristics and poor smoothness in mode switching during the cross-medium operation of amphibious wing-in-ground (WIG) craft, this paper develops an intelligent switching and control system based on multi-sensor fusion. The system integrates measurement data from the Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and Light Detection and Ranging (LiDAR) to construct a state observation module for mode switching decision-making. On this basis, a fuzzy switching logic centered on flight altitude and sea state characteristics is proposed, and a hybrid control strategy combining Linear Quadratic Regulator (LQR) and Proportional-Integral-Derivative (PID) is designed to realize the smooth transition between water surface and flight modes. Numerical simulations conducted in Python demonstrate that the proposed control scheme can accurately determine the mode switching timing, stabilize the pitch angle fluctuation within ±3° during the transition process, and effectively enhance the ability of WIG craft to operate autonomously and stably of the WIG craft in complex sea conditions.
Zhang, Qihao, Yan, Yifei, Cao, Fengshuo, Xiong, Yueling, Liu, Qing, Deng, Yanpeng, Wu, Peng
Intelligent Unmanned Aerial Vehicle System for Automated Red Pine Cone Harvesting in Dense Forests2026-99-063011/6/2026
This paper describes an intelligent unmanned aerial vehicle (UAV) system that automates red pine cone harvesting in dense forests to mitigate the safety and inefficiency of traditional manual harvesting. The suggested system combines multi-sensor fusion perception, variable-stiffness rigid-flexible composite manipulator, and smart path planning into a single aerial harvesting system. To create a three-dimensional model of the environment and robust cone detection under complicated lighting conditions and extreme canopy occlusion, a heterogeneous sensor suite such as LiDAR, depth cameras, and inertial sensors is used. According to the results of fused perception, collision-free approach paths are created to lead the UAV safely to target cones in limited three-dimensional spaces. One of the significant contributions of this work is the iterative design and optimization of a dedicated harvesting manipulator to be used in aerial forestry operations. A comparative analysis of various structural designs leads to the creation of a rigid flexible composite manipulator with an effective workspace diameter of 1.8 m and adequate stiffness to reduce the effects of the downwash of the UAV rotor. The disturbance-assisted harvesting strategy is adopted by the system, which enables efficient dislodgement of cones without requiring accurate grasping. The proposed technical route is proven to be feasible based on laboratory experiments and initial field tests carried out in real forest settings. It has been experimentally proven that the combined system can effectively perform autonomous harvesting cycles, including target recognition, navigation, and cone dislodgement, thus offering a practical technical paradigm of intelligent and safe forestry harvesting applications.
Shi, Shuaiqi, Du, Yisheng, Li, Xiang, Wang, Weidong
A Flight Test Anomaly Detection Framework Based on Adaptive Weight Fusion2026-99-060611/6/2026
Flight anomaly detection plays a critical role in ensuring aviation safety. However, conventional anomaly detection methods often suffer from limited accuracy due to their reliance on single-sensor observations or fixed-weight multi-sensor fusion strategies. To address these limitations, this paper proposes an adaptive weight fusion framework for flight-test anomaly detection. The proposed framework integrates a signal-to-noise-ratio-based adaptive weighting strategy, an improved Kalman filtering algorithm, and an ensemble anomaly detection model within a hierarchical fusion architecture to achieve robust multi-sensor data fusion and anomaly identification. Experiments were conducted using the NASA DASHlink Tail_666_9 flight dataset. Compared with conventional methods, the proposed framework achieved improvements in overall accuracy, precision, recall, and F1-score. Specifically, the proposed method achieved an F1-score of 0.486 while reducing the average anomaly detection delay by more than 2 s. These results indicate that the proposed framework can improve the effectiveness and timeliness of flight anomaly detection, demonstrating its potential for real-time flight safety monitoring.
Wang, Yixin
Enhancing SDV Architecture by Rethinking In-Vehicle Connectivity2026-28-021011/5/2026
The rapid evolution of automotive electronics has pushed traditional vehicle electrical and electronic (E/E) architectures to their limits. Conventional designs, characterized by point-to-point wiring harnesses, distributed domain-specific ECUs, and communication buses such as LIN, CAN, and Ethernet, impose significant drawbacks in terms of vehicle weight, integration complexity, and long-term scalability. As the industry moves toward the Software-Defined Vehicle (SDV) paradigm, there is a compelling need to rethink the foundational principles governing in-vehicle connectivity. This paper proposes a wireless-centric SDV architecture in which non-functional-safety-relevant sensors and actuators communicate over standardized wireless protocols, eliminating the need for their associated physical wiring. By decoupling these peripheral nodes from the wired backbone, the proposed architecture reduces harness bulk, lowers vehicle mass, minimizes redundant compute hardware, and significantly reduces integration cost. Safety-critical systems including those governing powertrain, braking, and steering are explicitly retained on hardwired, deterministic communication channels in compliance with ISO 26262 functional safety requirements. This paper additionally defines explicit assumptions and fallback behavior governing that wired/wireless boundary. The architectural framework is evaluated against key SDV design objectives such as modularity, over-the-air (OTA) updateability, and feature scalability, assessed qualitatively against published engineering precedent, with a structured validation roadmap defined to quantify latency, reliability, and coexistence performance in follow-on work. The analysis demonstrates that a hybrid wired-wireless topology can substantially simplify vehicle E/E architecture while maintaining the reliability and latency guarantees demanded by automotive-grade systems. This work contributes to a structured transition roadmap for OEMs and Tier-1 suppliers seeking to adopt SDV principles without compromising vehicle safety or regulatory compliance, together with an explicit protocol-selection framework and a functional-safety argument for the wired/wireless boundary.
Hebbale Ramkumar, Ramya, Sarkar, Dibas
Smart-ROPS — Machine Learning-Based Rear Occupant Protection System2026-28-014911/5/2026
Children dying in hot cars is not a new problem. Since 1998, more than 1,000 children have died this way in the United States alone — roughly 37 every year. That number has barely been reduced, which tells something alarming: the solutions built so far aren't solving it completely. Door-based reminders are a good example of why. They trigger when a rear door opens, but opening a door doesn't confirm a child was left behind. Single-sensor setups have their own gaps too; no single sensor handles every real-world scenario reliably on its own. Multi-sensor systems get closer, but most use rigid threshold-based rules that fall apart the moment conditions deviate — a sleeping infant barely moves, a child under a blanket reads completely differently on a thermal sensor. This paper proposes Smart-ROPS (Smart Rear Occupant Protection System), an embedded ML architecture that targets these specific failure modes without needing expensive hardware or complex software. Four sensor types feed the system: millimeter-wave radar, CO₂, thermal infrared, and ultrasonic. Data from these passes through three layers — the Perception Layer handles initial sensor fusion, the Processing Layer runs a decision tree as the primary classifier, and when confidence is low, a shallow neural network steps in for ambiguous cases. If a risk is confirmed, the Intervention Layer manages the response: starting with a smartphone alert and cabin ventilation, escalating to emergency services if needed. This is presented as an architectural design contribution — hardware prototyping and real-world testing are left for future work.
Shrimal, Harsh
An Optimization Framework for Dynamic Vehicle and Driver Fleet Allocation2026-28-014211/5/2026
EV fleets in the EU/US region are owned by a fleet manager who manages the entire fleet operation. Currently, the telematics ECU transmits a significant amount of information that can be used to our advantage for vehicle–route assignment. The beneficiaries of this solution would be the fleet managers, who will receive an optimized vehicle–route plan, and the drivers, who will be able to see their entire week’s schedule. This can be achieved using models and applications in operations research, which take in EV vehicle parameters and provide an optimal solution. Existing fleet and vehicle control strategies typically rely on fixed actuation limits or rule-based logic that are configured without direct awareness of fleet-level objectives or task-specific requirements. As a result, vehicles may operate inefficiently, experience unnecessary energy consumption, or incur accelerated component aging. There is therefore a need for a system in which fleet and task information directly influence vehicle actuation in a dynamic and coordinated manner. This approach enables fleet operators to reduce energy cost, battery degradation, and unplanned downtime by dynamically aligning vehicle actuation with actual task and fleet needs rather than static calibrations. It improves fleet utilization and scalability by allowing vehicles to automatically adapt their energy and power behavior as operational conditions change. As electrified and mixed fleets grow and complexity increases, such optimization-driven actuation becomes critical for achieving cost-efficient, reliable, and sustainable fleet operations. The scheduling is modelled in three distinct layers that is vehicle-task scheduler, charging scheduler, human driver scheduler, thereby ensuring a clean modular design.
Anavekar, Abhishek Anil, Bhowmick, Saikat
Distributed Regenerative Active Suspension System with Cross-Coupled Hydraulic Actuation and Road-Preview AI2026-28-016511/5/2026
This paper presents a Distributed Regenerative Active Suspension System (D-RASS) that integrates active suspension control with suspension-level energy regeneration in a predictive architecture. Each wheel employs an electro-hydraulic actuator interconnected through hydraulic cross-coupling and a shared DC bus, enabling coordinated force generation and bidirectional energy flow. Suspension forces are generated via controlled pressure differentials, while rebound energy is converted into electrical energy through motor–pump units operating in generator mode. Road-preview intelligence from LiDAR, radar, and geospatial data enables anticipatory suspension control. The centralized controller optimizes ride comfort, handling stability, and energy recovery by adapting force and energy flow strategies in real time. The proposed system establishes a unified framework for predictive suspension control and energy-aware vehicle dynamics.
Hazra, Sandip, Ambatkar, Kedar
Software Optimized Multicore ECU Architecture for Next Gen Vehicles2026-28-014411/5/2026
Modern vehicles increasingly rely on software-intensive functions, which continue to drive the growth and complexity of Electronic Control Units (ECUs). Conventional architectures based on single core or statically partitioned multicore microcontrollers are becoming inadequate for meeting emerging requirements such as deterministic real time performance, high communication throughput, synchronized time bases, and secure message handling. This paper introduces a software driven multicore consolidation approach that enables multiple ECU functions to be integrated on a single high-performance microcontroller while maintaining essential attributes of safety, determinism, and responsiveness. The proposed architecture emphasizes the co-location of dependent software modules to reduce cross core communication overhead and enhance runtime efficiency. By leveraging AUTOSAR compliant multicore Basic Software (BSW) stacks, including Communication, NVM, STBM, Diagnostics, and OTA, the system supports improvements in timing accuracy, data throughput, and task synchronization. In addition, core-specific communication channel assignment, interrupt-based routing, HSM supported secure frame handling, and multicore enabled NVM with a master–satellite design collectively contribute to more efficient CPU utilization and reduced latency under high load communication scenarios. The approach supports key industry relevant capabilities (e.g., runtime remote software updates, sensor fusion) and enables efficient handling of high-volume communication activity, making it adaptable for emerging multicore ECU architectures in next generation vehicles. The theoretical analysis presented in this paper describes how the proposed software driven consolidation can reduce system overheads, improve the organization of multicore workloads, and support more structured parallel execution. The study further highlights that a well-designed multicore software strategy can serve as a practical and scalable direction for future automotive electronics, supporting higher levels of feature integration and communication flexibility, along with lower CPU load compared to traditional multicore ECU implementations. Considerations for future enhancements in software component grouping and communication stack adaptability are also discussed.
Sadhu, Santosh, Toshniwal, Sachin
A CI/CD-Integrated Digital Twin Framework for Scalable Validation of Automatic Transmission Application Software Using Platform-Based Architecture2026-28-018111/5/2026
Modern automatic transmission (AT) application software validation is predominantly dependent on Hardware-in-the-Loop (HiL) systems, resulting in significant time, cost, and scalability limitations within continuous integration/continuous deployment (CI/CD) workflows. These constraints intensify with increasing software complexity and the need to support multiple transmission variants across evolving hardware configurations. Existing validation approaches lack a scalable, software-centric surrogate capable of representing both control logic and physical system behaviour under real-time operating conditions. This study proposes a digital twin-based validation framework for AT application software, integrated within CI/CD pipelines. The framework restructures application software into a platform-based architecture enabling reuse across multiple transmission programs. A coupled plant model is developed to simulate drivetrain dynamics, ensuring system-level fidelity. The architecture also permits future data-driven augmentation for transient response correction and extended scenario coverage; however, the present study focuses on the baseline digital twin, hardware abstraction, and CI/CD validation workflow. A key contribution is the introduction of a hardware-adaptive abstraction layer, enabling the digital twin to accommodate variations in sensors, actuators, and control interfaces without full HiL dependency. Across 15 scenario hardware validation combinations, the reduced-order digital twin achieved a runtime gain of 1.20×–1.68× over the HiL proxy execution, with an average gain of approximately 1.46× and an average execution-time reduction of approximately 31% per validation case. The study demonstrates that integrating application software, plant models, and hardware-aware abstraction within a digital twin enables a transition from hardware-constrained validation to software-scalable paradigms. The framework provides a pathway toward efficient and adaptable validation in software-defined vehicle ecosystems. The proposed framework is aligned with ZF’s platform-based transmission software strategy, enabling reuse across multiple automatic transmission programs and supporting scalable validation of ZF powertrain control systems under evolving hardware configurations.
Gavara, Chandrakant, Sali, Rohan, Peddibhotla, Brahmaprakash, Wadkar, Swapnil P.
Self-Regulation of Force in the Space Truss-Type Deployable Structure Based on Magnetic-Electric Material2026-99-234110/30/2026
Under on-orbit disturbances, large space truss-type deployable structures readily undergo vibration. Such vibration may degrade pointing accuracy and positioning precision. As a countermeasure, an internal-force self-regulation method is proposed using a magnetoelectric self-sensing actuator. This actuator combines Terfenol-D with piezoelectric material, thereby realizing force output and self-sensing. It can be placed at critical truss joints. By exploiting the ΔE and Villari effects of magnetostrictive materials, a resonant-force self-sensing mechanism is established. The actuator model is then integrated into the deployable truss’s dynamic model, and an LQR-based active vibration suppression strategy is derived. Simulation results show that the actuator detects static and dynamic output forces. Over a specific magnetic field range, sensing behavior exhibits an approximately linear dependence on the output force. Control outcomes confirm effective suppression of structural vibration, reduced response amplitudes at key nodes, and enhanced structural stability. Therefore, a novel method for vibration suppression in large deployable truss-type space structures is provided by these findings.
Xie, Dongjian, Ni, Xiaofeng, Cheng, Lei, Cui, Qifeng, Zhang, Yahui
Synergistic Enhancement of Impact Resistance in SiCw-Polymer Composite2026-99-232510/30/2026
Addressing the performance degradation bottleneck of conventional impact-resistant materials under complex operating conditions, and the limitation of existing research focusing primarily on enhancing single properties while neglecting material equilibrium, this study employs silicon carbide whiskers (SiCw) as the reinforcing phase. Through surface modification techniques, SiCw/celluloid and SiCw/polyimide dual-polymer composite systems were constructed and systematically investigated. Surface modification of SiCw was achieved using titanate and silane coupling agents. Through mechanical testing and X-ray photoelectron spectroscopy (XPS) characterisation, the effects of SiCw loading and modification treatments on composite mechanical properties and interfacial bonding were analysed. Results indicate that SiCw introduction significantly enhances the tensile, flexural, and impact strength of the polymer matrix, with optimal addition ratios identified: 6% for the celluloid system and 1.0% for the polyimide system. Surface modification further optimises toughening effects by reducing surface oxide layers and impurities on SiCw particles while strengthening interfacial bonding. This study provides practical guidance for the system design of high-performance impact-resistant composites. The resulting materials hold broad application prospects in sectors demanding high structural impact resistance, such as aerospace and transportation.
Xue, Kaiming
Feature Fusion and CNN-Based Acoustic-Emission Diagnosis for Aero-Engine Initial Damage2026-99-231610/30/2026
Acoustic emission (AE) signals generated by incipient aero-engine damage are usually weak, intermittent, and highly dependent on sensor position, which makes diagnosis based on a single channel unreliable. To address this issue, this paper develops a multi-sensor AE diagnosis framework that combines entropy-guided feature fusion with a convolutional neural network (CNN). Time-domain and frequency-domain descriptors are first extracted from AE waveforms collected by multiple sensors. The consistency of each feature type across channels is then evaluated using feature entropy. Features with lower entropy are considered more informative for damage discrimination and are therefore retained for fusion. Based on the normalized channel contributions of these selected features, an adaptive weighted fusion matrix is constructed and used as the input to the CNN. The method is validated on a self-built aero-engine structural test rig equipped with 12 AE sensors. Fifteen representative initial-damage cases are designed, including signal excitation, crack-like excitation simulated by pencil-lead break (PLB), and impact-induced micro-deformation cases. Experimental results show that the proposed framework achieves a best average classification accuracy of 96.80% and provides more balanced class-wise performance than conventional machine-learning and deep-learning baselines. Additional noise-injection tests further indicate that the method maintains good robustness over a wide signal-to-noise-ratio range. These results suggest that entropy-guided multi-sensor AE fusion is a practical solution for intelligent diagnosis of aero-engine initial damage.
Jin, Yucheng, Han, Cong, Zhang, Leixuan, Yang, Guoan
Multiscale MD-FEA Investigation on Large-Deformation Mechanical Behaviors of Hydrogels Modified with Ionic Liquids2026-99-230610/30/2026
Hydrogels incorporated with ionic liquids (ILs) demonstrate broad application prospects in the fields of wearable flexible sensors and human physiological monitoring. Nevertheless, the intrinsic correlation between microscale intermolecular interactions and macroscale nonlinear mechanical responses under large tensile deformation has not yet been fully clarified. In this work, a coupled numerical framework integrating molecular dynamics (MD) and finite element method (FEM) was adopted to explore the mechanical behaviors of poly(acrylic acid)/N,N’-bis(acryloyl)cystamine/ionic liquid (PAA/NB/IL) hydrogels under large deformation. A series of molecular models with graded IL doping levels were established to characterize the structural rearrangement and energy change characteristics during uniaxial tension. Numerical results from MD simulations reveal that the addition of ILs strengthens non-covalent interactions such as van der Waals forces and electrostatic attractions, and further drives covalent bond stretching and angular deformation to bear external loads under high-strain conditions. Meanwhile, a generalized Maxwell constitutive model, whose Prony-series parameters were fitted by experimental stress-relaxation data, was introduced into FEM numerical calculations. The simulated quasi-static tensile properties are highly consistent with experimental measurements. Comprehensive analysis demonstrates that the mechanical enhancement induced by ILs stems from the synergistic effect of covalent structural deformation and the reconstruction of non-covalent interactions, which offers valuable theoretical guidance for the development of hydrogel materials applied in large-deformation engineering scenarios.
Li, Sitan
A Novel Sliding Mode Control Strategy for an Electro-Hydrostatic Actuator Based on Singular Perturbation Theory2026-99-235510/30/2026
The Electro-Hydrostatic Actuator (EHA) is a high-order nonlinear system with notable parameter uncertainties and coupled electro-hydraulic dynamics. To achieve high-precision position control, this paper proposes a novel sliding mode control strategy based on singular perturbation theory (SPT-SMC). The singular perturbation theory is applied to achieve a rational order reduction of the EHA system, simplifying controller design while mitigating unmatched disturbances. A sliding mode controller incorporating a new reaching law is then designed based on the reduced-order system. Finally, simulations studies are executed to assess the SPT-SMC. The results demonstrate that, compared with widely used control strategies, the proposed approach achieves faster response while ensuring stability.
Wang, Junyi, Wang, Wenshan, Hao, Juan, Bai, Liangliang, Du, Shihui
An Electromagnetic-Thermal-Mechanical Coupling Model of the Three-Coil Wear Debris Sensors for Wide-Temperature and Kinematic Analysis2026-99-298510/19/2026
Real-time monitoring of metal wear debris in lubricating oil is crucial for accurately assessing aerospace equipment health and preventing catastrophic mechanical failures. Although three-coil differential inductive sensors are widely used due to their high sensitivity and robust structure, their detection performance inevitably degrades under the influence of multi-physics coupling effects inherent in complex industrial environments. Existing analytical models were primarily limited to single electromagnetic field simulations, failing to capture the intricate electromagnetic-thermal-mechanical interactions, which reduced analytical accuracy and hindered the optimization of sensor designs for extreme conditions. To overcome these challenges, this study established a comprehensive parametric simulation model to conduct a rigorous electromagnetic-thermal-mechanical coupled analysis of the differential sensor. Specifically, the research analyzed the underlying mechanisms by which wide temperature variations from -55 °C to 200 °C and specific debris kinematics exert varying degrees of influence on the induced voltage. Quantitative results indicated that both the isolated thermal and mechanical fields altered the voltage by a maximum of 0.649%, demonstrating that the sensor maintains a relatively stable response across extreme thermal gradients. Furthermore, the analysis revealed that increased debris velocity decreased the induced voltage amplitude due to the intensified motion-induced eddy current effects. Notably, in complex multiple debris scenarios, the strong dominant signals generated by ferromagnetic wear debris severely masked the weaker responses from non-ferromagnetic ones, indicating a substantial missed detection risk under mixed contamination conditions. Ultimately, this research successfully filled the critical simulation gap for evaluating these sensors under multi-physics coupling, providing robust theoretical guidance for their reliable deployment in complex industrial applications.
Shi, Xinfa, Duan, Chenhao, Zhen, Huanyi, Zhao, Yihe, Li, Min, Wang, Xiaozhang, Zhang, Qing, Li, Zhikang, Zhao, Libo
ARET-ALSTM: Intelligent Beam Prediction and Dynamic Compensation Network for Aerial Base Stations under Non-Stationary Turbulent Conditions2026-99-295410/19/2026
With the unprecedented surge in demand for flexible deployment and ultra-high throughput in next-generation wireless networks, airborne base stations (ABS), particularly unmanned aerial vehicles (UAVs), have become emerged as indispensable mobile communication infrastructure. To facilitate high-gain directional transmission within the millimeter-wave band, ABS frequently employ massive MIMO antenna arrays. However, under random disturbances caused by non-stationary turbulence, the system faces a severe physical bottleneck: weak anti-interference capability due to narrow beamwidth, which can easily lead to a sharp drop in link capacity. Addressing the two major shortcomings of existing deep learning prediction schemes—susceptibility to sensor noise interference and negative capacity performance shifts during stable periods, and response lag due to memory inertia during sudden turbulence periods—this paper proposes an ARET-ALSTM active compensation network based on SNR-aware multimodal attention, agile reset and adaptive event-triggered mechanisms. This network incorporates an event-triggered mechanism governed by a dynamic physical variance threshold, adaptively executing compensation during stable intervals to prevent excessive overshoot and redundant computational overhead. Simultaneously, it employs an agile feature reset mechanism based on an adaptive sliding window for concept drift detection, forcibly discarding old memories and reconstructing attention weights during sudden wind shear, achieving millisecond-level ultra-fast adaptation and directly driving network optimization with the goal of maximizing communication capacity. Simulation experiments show that, under different array configurations ranging from 2×2 to 16×16 and complex flow conditions, ARET-ALSTM successfully overcomes the physical capacity collapse limitation under large antenna scales, and its tracking agility and robustness are significantly better than benchmark algorithms such as Transformer, RNN and traditional Kalman filtering.
Tu, Jiajun, Zhou, Jihua, Li, Yanhang, Zhao, Tao, Huang, Hua, Wen, Yuanbin, Guo, Ziwen
Magnetometer Temperature Compensation of Geomagnetic Directional Technology2026-99-295710/19/2026
Accurate compensation of magnetic measurement errors is one of the key factors in ensuring the accuracy of geomagnetic navigation. Magnetic sensors are susceptible to temperature drift during use due to the disturbance of external ambient temperature, which affects the accuracy of magnetic field measurement. To improve the precision of the tri-axis geomagnetic sensor, a suitable thermal error compensation algorithm must be designed to process measured data correctly. Thus, a temperature compensation model and a high-precision geomagnetic directional technology were proposed in this paper. Firstly, a tri-axis geomagnetic sensor temperature compensation model based on particle swarm optimization-least squares support vector machines (PSO-LSSVM) was established. Then the inertial measurement unit (IMU) data is processed with zero drift correction and filtering. Finally, geomagnetic directional experiments were conducted with the gradient descent method. The experimental results show that the proposed temperature error compensation method can effectively alleviate the impact of thermal variations on the detector output and enhance the precision of the geomagnetic orientation system. It is expected to be applied to multiple navigations, aviation, and aerospace fields.
Fan, Chenyu, Guan, Feng, Liang, Zhuo, Zhang, Haifeng
Research on Temperature Hysteresis Error Suppression Technology for Capacitive MEMS Accelerometers2026-99-296810/19/2026
The performance of MEMS accelerometers is highly sensitive to temperature variations, manifesting as zero drift, sensitivity drift, and hysteresis error. This paper presents a low-temperature-drift, low-noise interface ASIC for capacitive MEMS accelerometers. The circuit employs an analog closed-loop architecture comprising a small-capacitance front-end detection circuit, a variable gain amplifier (VGA), a correlated double sampling (CDS) circuit, a PID controller, and a feedback module. To suppress temperature-induced errors, key optimizations are implemented: the charge amplifier employs active and passive charge compensation techniques to reduce common-mode offset drift; the VGA uses switched feedback resistors for gain adjustment; a Class-AB unity-gain buffer enhances driving capability while isolating load interference; and a high-order compensated bandgap reference with pre-regulation achieves low temperature drift and high power supply rejection. Post-layout simulations demonstrate a system sensitivity of approximately 2.27 V/g, a sensitivity temperature coefficient of 74.55 ppm/°C, and a 1σ zero-bias temperature drift of 1.757 mg. Experimental results over the full temperature range from -45°C to 85°C show an output hysteresis error of only 0.6 mg. These results validate the effectiveness of the proposed temperature hysteresis error suppression technique, providing a valuable reference for the design of high-precision MEMS accelerometer interface circuits.
Zhao, Weiqiao, Liu, Guowen, Hao, Zhiwen, Li, Zhaohan, Gao, Naikun, Zhang, Wenbo, Fu, Qiang, Yin, Liang
Research on Nonlinearity of Interface ASIC for MEMS Accelerometer with Switched-Capacitor Closed-Loop Feedback2026-99-296910/19/2026
This paper investigates the nonlinearity issues in the interface ASIC system for capacitive MEMS accelerometers. First, the sources of nonlinearity in the mechanical structure of the accelerometer and the switched-capacitor interface circuit are analyzed, including the nonlinearity of mechanical displacement-to-capacitance conversion, harmonic distortion caused by parasitic capacitance, and the contradiction between range and linearity due to a low-stiffness proof mass in open-loop mode. To suppress the nonlinearity, PID control is adopted to realize closed-loop electrostatic force feedback, a loop error model is established, and a capacitor array compensation circuit is introduced to eliminate the output offset caused by parasitic capacitance. The interface ASIC is designed in a 0.18 μm BCD process, and the layout design and tape-out are completed. Pre-simulation results show a nonlinearity of 0.0124% within a 0–50 g range; post-simulation nonlinearity is 0.0657%. In actual tests, the accelerometer achieves a scale factor of 0.091 V/g over the 0–50 g range, the maximum residual of the least-squares fitting is 0.000645 V, and the nonlinearity is 0.0728%, approximately 160 ppm. This study verifies the effectiveness of closed-loop PID control and capacitance compensation in suppressing nonlinearity, providing a theoretical basis and engineering reference for the design of high-linearity MEMS accelerometers.
Gao, Zhi, Li, Zhaohan, Hao, Zhiwen, Liu, Guowen, Gao, Naikun, Zhang, Wenbo, Fu, Qiang, Yin, Liang
Noise Analysis and Circuit Optimization for the Drive Loop of Resonant Accelerometers2026-99-296710/19/2026
Resonant MEMS accelerometers are core inertial devices in precision measurement fields such as inertial navigation, aerospace, and seismic monitoring, characterized by frequency signal output, strong anti-interference capability, and high-precision potential. With the growing demand for sub-microg-level measurement accuracy in high-end industrial and scientific applications, noise suppression and detection sensitivity optimization have become key research topics for resonant accelerometers. This paper conducts a systematic modeling study on resonant accelerometers, analyzes their mechanical structure and acceleration sensing mechanism based on the force-frequency effect of double-ended tuning forks, and establishes corresponding mathematical models. Non-ideal noise models of the mechanical structure and drive loop are constructed separately, and key noise source analysis identifies mechanical thermal noise and 1/f noise of the front-end charge amplifier as the main contributors to system noise. To solve these problems, a dedicated low-noise charge amplifier is designed for the drive loop. Simulation results show that the input-referred noise of the low-noise operational amplifier used in the charge amplifier reaches 18.804 nV/√Hz @ 50 kHz. When applied to the drive loop and verified through tape-out testing, the system achieves a phase noise better than -119.904 dBc/Hz @ 20 Hz offset from the resonant frequency, with a corresponding acceleration noise density of approximately 0.477 μg/√Hz @ 20 Hz.
Cai, Hebin, Li, Zhaohan, Zhang, Qiukun, Liu, Guowen, Zhang, Wenbo, Yin, Liang, Gao, Naikun, Fu, Qiang
A Multi-Platform Bearing-Only Localization Method Based on Confidence Interval and Error Compression Iteration2026-99-294610/19/2026
Due to the fact that passive underwater acoustic signals can usually only obtain the bearing information of the target and are subject to complex ocean environmental interference, which poses significant obstacles to target localization using a single platform, this study intends to employ multiple unmanned underwater vehicles for cooperative localization of underwater targets. To address the issues of systematic bias in bearing measurements, high nonlinearity, and unknown target motion states in complex underwater acoustic environments, this paper proposes a bearing-only localization method based on confidence interval and error compression iteration. The unknown motion process of the target is mapped into a region expansion process in space, and the cooperative localization of multiple platforms is used to finely compress the regional error, achieving bearing-only localization of underwater targets through gradual region compression. The core idea of the proposed method is to map the unknown motion state of the target into an expansion process of an uncertainty region. First, bearing-only measurements obtained by multiple platforms at different time instants are used to construct sector regions with systematic bias. Then, a likelihood function is employed to update the probability distribution over the spatial grid, enabling iterative error compression. Meanwhile, the 95% highest posterior density interval is introduced as the statistical output of the localization results. Simulation experiments were conducted under three different initial scenarios. The results show that, with bounded bearing measurement errors, the proposed method can compress the area of the 95% HPD region from approximately 107 m2 to the order of 104 m2 within 120 seconds, while the localization error converges from several kilometers to less than 40 meters.
Huang, Fengfeng, Wang, Yuxuan, She, Tao
Active Control of Attitude-Maneuvering-Induced Vibration of Membrane Antenna Based on Reinforcement Learning2026-99-290810/19/2026
Space membrane structures, prized for their lightness, affordability, and ability to fold and unfold, hold immense promise for missions like deep space exploration, high-definition Earth observation, and advanced satellite communications. However, these structures are typically large-size, ultra-lightweight, highly flexible, and have very low damping. This leads to complex dynamic behavior featuring significant geometric nonlinearity, strong coupling between rigid and flexible motions, and uncertain parameters. Consequently, creating accurate yet simple mathematical models for designing controllers is exceptionally difficult, and traditional control methods that rely on such models often fall short. To tackle this problem, this study focuses on a membrane antenna spacecraft equipped with cable actuators, aiming to suppress its vibrations during attitude maneuvers. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, an intelligent vibration controller based on the Actor-Critic architecture is designed. This approach is data-driven, meaning it learns effective control strategies directly through interaction with the system’s environment, bypassing the need for a precise pre-defined mathematical model. Simulation results show that the controller demonstrated superior vibration suppression and strong adaptability across several key scenarios.
Zhang, Hua, Chen, Cheng, Liu, Xiang
Design of Closed-Loop Self-Excited Drive Circuit for Resonant Accelerometers2026-99-297010/19/2026
Resonant MEMS accelerometers have important application value in the field of high-precision inertial detection due to their excellent characteristics such as quasi-digital output, high resolution and low power consumption. The drive loop and interface circuit are the core factors determining their detection accuracy and operational stability. This paper deeply investigates the force-frequency sensitive mechanism of resonant accelerometers, establishes a mass-spring-damping dynamic model and an RLC equivalent electrical model, and completes the full-process design of the closed-loop self-excited drive loop and interface ASIC chip based on 0.18 μm CMOS technology. Key modules including a high-gain operational amplifier, an integrated static error detection circuit with full-wave rectification and PI control, a fully differential nonlinear multiplier, a low-noise charge amplifier, and a low temperature coefficient bandgap reference are designed and optimized. A closed-loop control system is constructed to realize rapid start-up and constant-amplitude oscillation of the resonant beam. Simulation results show that the temperature drift of the bandgap reference circuit is 4.6482 ppm/°C over the full temperature range; the charge amplifier achieves a gain of 102.128 dB with a low-frequency thermal noise of 10.33 nV; the drive loop can be stably established within 100 ms, and the output fluctuation amplitude of the PI controller is 284.6 μV; The total current of the circuit is 2.44 mA, and the power consumption is 12.2 mW. The interface circuit designed in this paper has the advantages of low noise, high integration and high precision, which meets the high-performance application requirements of resonant MEMS accelerometers and provides reliable technical support for the engineering implementation of the device.
Tong, Jianghao, Liu, Guowen, Zhang, Qiukun, Li, Zhaohan, Gao, Naikun, Zhang, Wenbo, Yin, Liang, Fu, Qiang
Aerospace & Defense Technology: Oct 202626AERP1010/8/2026
Terabit Optical Communications in Space: Engineering High-Speed Networks for Data Centers in Orbit Mild Hybrids: A Current-Gen Bridge to Electrifying Military Ground Vehicles The Integrated and Flexible Approach to Large-Scale Metrology: How Integrating Laser Trackers and Modular Arms Creates a 'Unified Nervous System' The Defense Industry Continues to Meet CMMC Phase II Requirements Despite 60-Day Pause In Space, No One Can Hear You Stream AMD to Bring UCIe Connectivity to Select Versal Adaptive SoCs How Special Interface Gages Provide Microscopic Precision for Designing Ballistic Missiles The pristine precision needed to certify the nation's underwater nuclear deterrent is anchored on the talents of civilian scientists at a Navy laboratory in Southern California. Rapidly Testing Deployable Systems Sandia National Laboratories is evaluating portable security architectures that combine radar, drones, ground sensors, cameras and wireless communications networks into a single rapidly deployable system for remote and austere environments. Testing focuses on sensor integration, resilient communications and edge-based data processing to improve situational awareness while operating within real-world bandwidth constraints. Tailored Surface Vibrations to Improve Aerodynamic Performance in Passenger Jets University of Colorado Boulder researchers have demonstrated two new phononic flow-control concepts, super-resonance and scatterless interference, that use engineered subsurface vibrations to interact with boundary-layer airflow across a broader range of turbulence frequencies than previously possible. By delaying flow instabilities without altering a wing's external shape, the technology could reduce aerodynamic drag and improve fuel efficiency in passenger aircraft. New Type of Attack Can Slip Past the Defenses in Your Computer's Processor MIT researchers found a way to exploit a split-second gap in chip security - and used it to acquire a Linux system's password file. Army Tests Safer Electrical Connectors for Ground Vehicle Readiness U.S. Army DEVCOM Ground Vehicle Systems Center engineers are evaluating cadmium- and hexavalent chromium-free electrical connectors designed to reduce hazardous material exposure while maintaining corrosion protection, electrical performance, and long-term vehicle sustainment.
Automotive Engineering: October 202626AUTP1010/8/2026
Why cars need hearing before they need more vision An engineering perspective on faster intent, lower compute, and better in-cabin UX. Improved laser welding for automotive seat structures and recliners When it comes to automotive seats, regulatory pressures aren't the only challenge. Of ladders and limits Why supply chain leaders still can't scale their warehouse technology. Lidar at $200: The competitive inflection point Western OEMs can't ignore Chinese suppliers Hesai and Robosense show one way to make rapid advances in sensor technology. Accelerating air suspension development with a deep-learning surrogate for FEA A bespoke AI tool compressed a typical air spring development cycle from several weeks to around an hour and uncovered a feasible design solution that manual iteration had previously ruled out. Trailblazing innovators: Meet Rising Star winner Qiang Li Meet the winner of the third annual SAE Media Group's Women in Engineering: Rising Star Awards program, which proudly honors women engineers who are driving innovation and making significant contributions to the engineering profession. Editorial AI, Automechanika, and a Rising Star The Navigator 2027 could be the Year of the EREV in America Automechanika Frankfurt: re-thinking automotive materials with INDRA Clarios' battery management system for fleets now in 2,000 trucks Advanced Manufacturing Commercialization Center holds open house for potential partners Designing flash storage platforms with an eye to SDVs LGES opens first-of-its-kind battery plant in Michigan Mercedes Maybach S-Class first drive: V12 pampering for all occupants Product Briefs Spotlight: Plastics & composites; electrification technology Q&A Faster than anyone else thanks to H2 ICE
Streamlined Model Composition for OneSAF for Use in Rapid Trade Study Data Collection and Analysis2026-01-75479/22/2026
This research is meant to enhance the analytic capabilities of OneSAF for usage as a Monte Carlo-style data generator for use in large problem space trade studies. Using novel ground vehicle data such as: RHA armor values, weapon penetration prediction models, and sensor values, new models can be developed in OneSAF for use in data generation and analysis. This process and companion software developed for this purpose enables the rapid construction and evaluation of differing vehicle variants in a fraction of the time of the baseline process, improving the efficiency of using OneSAF as a data analysis tool. This approach facilitates a more comprehensive virtual experimentation approach that can use manufactured data as a part of the process.
Sapunkov, Oleg, Roberts, Bradshaw, Jorgensen, Maxwell
Leveraging Standards-Based MOSA for SWaP-Constrained Deployable Systems with VNX+2026-01-75369/22/2026
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Littlefield, Mark
Human Digital Twin Architecture for Ground Vehicle Scenario Integration2026-01-75059/22/2026
Ground vehicle commanders operate in scenarios which bare high cognitive load. They must be reactive to time-critical events where attention is divided between a variety of sensors, crew members, the physical world, and digital displays, which can result in missed situational cues. This paper presents a Human Digital Twin (HDT) architecture which provides real-time, embodied AI assistance to commanders in a military ground vehicle simulation scenario. The system integrates a data pipeline for combining a MetaHuman avatar in Unreal Engine with multi-modal data ingestion and a large language model (LLM). In addition, a retrieval-augmented generation approach grounds the LLM with mission-specific context, and a Big Five personality framework for prompt design constructs a consistent agent persona throughout the scenario. The architecture is demonstrated with a prisoner of war camp scouting mission, in which the HDT selectively intervenes when needed to alert the commander to critical events when missed. A system latency evaluation is provided to demonstrate viability for real-time integration. Results show the potential of integrated HDT systems to improve situational awareness and decision support in high stakes ground vehicle operations.
McCarthy, Martin, Mohammed, Abdul Mannan, Gallagher, Reese, Neumann, Carsten, Bruder, Gerd, Reiners, Dirk, Cruz-Neira, Carolina, Paul, Victor
SOSA Enabled Automotive Technologies Disrupt the Battlefield2026-01-75359/22/2026
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Pilaud, William J.
Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles2026-01-75509/22/2026
Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.
Ross, Timothy, Boone, Julia, Afghah, Fatemeh
An Assessment of Biomanufacturing in Forward Deployed Locations2026-01-75579/22/2026
Biomanufacturing uses microorganisms to produce chemicals or materials of interest, much like a brewery uses fermentation by yeast to produce the alcohol in beer. Biomanufacturing relies upon synthetic biology to reprogram yeast or other microorganisms to produce something of greater value, such as fuel, food, or pharmaceuticals. Industrial biomanufacturing has made significant advances and the products it can deliver include reactive coatings and textiles, sensors, optical materials that can bend light, and new therapeutics such as antimicrobials and vaccines. The convergence of synthetic biology, robotics, and artificial intelligence is opening the way to produce materials never before possible in the commercial market. These same technologies create the opportunity for the miniaturization of this technology to fit into ever more compact spaces, bringing forward deployment of these mini-factories closer and closer to the point of need.
Ahern, Brooke, Crumbley, Annie, Walker, Anne, Grodecki, Joseph
Decision-Centric Virtual Metrology for Geometric Deviation via Foundation-Embedding Retrieval2026-01-75549/22/2026
Modern defense manufacturing and sustainment require timely engineering decisions (e.g., inspection triage, rework/accept decisions, and process adjustment) based on the as-built geometric quality. Advanced machining systems generate rich multichannel controller and sensor streams, yet accurate geometric deviation labels remain costly and delayed because they depend on downstream metrology. This paper introduces ChronosGD, a retrieval-based virtual metrology framework. ChronosGD predicts pointwise geometric deviation from multichannel time series data by: (1) retrieving the most similar historical process windows in a frozen Chronos-2 embedding space, and (2) transferring deviation information through similarity-weighted aggregation. ChronosGD avoids plant-specific gradient retraining during deployment; adaptation is achieved by refreshing a labeled historical memory as new inspected parts become available, while preserving traceability through explicit neighbor provenance.
Hoang, Danny, Matthiessen, Ryan, Miller, Christopher, Mannan, Nasir, ElKharboutly, Ruby, Gorsich, David, Castanier, Matthew P., Imani, Farhad
Development of a UAV Platform for Remote Inspection and Visualization2026-01-75119/22/2026
This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.
Lee, Yeen K., Bainard, Sean, Shaughnessy, Michael, Bolger, Matt, Koepp, R. Tucker, Salehzadeh, Roya, Mallory, Stephen, Mynderse, James A., Guillen, Pedro, Hernandez, Margarita
Clemson Off-Road Dataset (CORD): A Multimodal Benchmark for Autonomy beyond the Flat-World2026-01-75189/22/2026
While autonomous perception has matured within the structured confines of urban roadways, it remains brittle when confronting the chaotic, non-rigid terrain of the natural world. This paper introduces the Clemson Off-Road Dataset, a high-fidelity, multimodal dataset engineered to bridge this gap by challenging standard “flat-world” assumptions. Featuring 2.90 TB of sensor data, the dataset captures a diverse spectrum of unstructured environments, ranging from the transitional trails of CU-ICAR and the day/night lighting dynamics of TN3 to the unstructured wilderness of Camp Daniels and the novel coastal scenery of Edisto Island. Distinguishing itself from existing forest-centric benchmarks, the Clemson Dataset provides a first-of-its-kind focus on coastal data, featuring unique adversarial conditions such as extreme solar glare, loose sand, and shifting tide lines. The data is collected aboard a Polaris RZR Pro R 4, a high-performance platform integrated with a sensor suite designed to perceive physics beyond geometry. Alongside 360° HD camera coverage, 3D LiDAR, and Radar, we integrate Cubert Ultris Hyperspectral imaging and Prophesee EVK4 Event-based vision to enable material classification and high-dynamic-range motion tracking. To overcome the bottleneck in ground truth generation, we used our “AI LabelMate,” a context-aware semi-automated annotation agent that fuses Vision-Language Models (Florence-2) with SAM2 to generate 6331 pixel-perfect annotated frames using a specialized off-road ontology and a human-in-the-loop pipeline. We establish performance baselines using Oneformer for semantic segmentation and used SalsaNext for lidar point clouds labelling. Available in both raw ROS2 bag and extracted standard formats, this Dataset serves as a pivotal testing ground for the next generation of robust autonomous systems.The dataset of this paper is available upon request to the Virtual Prototyping of Autonomy-Enabled Ground Systems (VIPR-GS) Center.
Patil, Ashish, Gupta, Prakhar, Bhosale, Mayuresh, Mukwaya, Arthur, Jegede, Akinbobola, Mikulski, Dariusz, Mwakalonge, Judith, Jia, Yunyi
Counterfeit Avoidance and Quality Assurance: Supply Chain Screening of Cots Microelectronics in Tactical System2026-01-75239/22/2026
This paper details the successful scaling demonstration of a comprehensive supply chain screening process for commercial off-the-shelf (COTS) motherboard subassemblies used in tactical servers for naval applications. Our approach leverages Power Fingerprinting (PFP) technology, which uses unintended analog emissions and machine learning to provide independent, non-destructive, and scalable integrity assessment of microelectronics. The primary goal of the effort was to demonstrate the effectiveness and scalability of the PFP screening process without disrupting or delaying the manufacturing workflow. The screening successfully detected hardware and firmware modifications and identified two cases of abnormal behavior: unusual BIOS power reset and elevated CPU sensor readings on two motherboard subassemblies. Following our quality control forensic analysis, we determined the root cause of these anomalies and their potential impact on the host platform.
Aguayo Gonzalez, Carlos R., Roberson, Ken
Designing a Digital Twin–Enabled Requirement Verification Workflow within an MBSE Framework2026-01-75309/22/2026
Verification of functional requirements in Model-Based Systems Engineering environments remains fragmented across heterogeneous tools and manual processes. This paper presents a digital twin–enabled workflow that supports automated requirement verification through integration of SysML models, executable simulation environments, and verification evaluation functions. Within this scope, the objective is to formalize a verification workflow that preserves architectural abstraction while enabling automated, traceable, and simulation-driven evaluation of functional requirements. The approach establishes a continuous digital thread that maintains traceability between requirements, system architecture, and verification outcomes. The workflow is demonstrated using a differential-drive robotic platform, where sensor data availability and update rate verification are used as representative examples of digital twin-based functional requirement evaluation. Results illustrate the feasibility of incorporating digital twin-driven verification into model-centric engineering processes while maintaining consistent verification feedback within the system model. The demonstration produced both passing and failing verification outcomes, illustrating the workflow’s ability to surface requirement-design mismatches.
Zeki, Omar, Sahebsara, Farid, Torkjazi, Mohammadreza, Hieb, Michael R., Raz, Ali K.
Deterministic Multi-Modal Sensor Ingestion and Actuation via Holoscan-Integrated Switched Fabrics2026-01-75379/22/2026
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Reardon, James M.
Simulation-Based Development and Evaluation of Heat-Quantity-Based Thermal Operating Strategies for Automotive Applications2026-24-00129/21/2026
Thermal management of hybrid electric vehicle (HEV) powertrains requires the simultaneous conditioning of multiple components operating at fundamentally different temperature levels. For thermal management systems, which directly couple the thermal circuits of the internal combustion engine (ICE), electric motor and inverter (EMINV), and traction battery (BAT) for example via controllable three-way valves and a ring-circuit, the decision of when and which components to couple has a direct impact on overall powertrain efficiency. Existing thermal operating strategies rely on empirically defined temperature thresholds and fixed component priority rankings, without quantifying the actual efficiency benefit associated with each coupling decision. This paper presents the development and simulation-based evaluation of a heat-quantity-based thermal operating strategy for a prototype HEV at TU Darmstadt. The strategy introduces three new computational modules — a Q-Indicator quantifying the thermal surplus or deficit of each component, an η-Indicator evaluating real-time component efficiencies as a function of temperature and operating point, and a Δη module computing the combined efficiency gain of each potential coupling pair prior to actuation. Coupling is executed only when the combined efficiency delta is positive, replacing empirical prioritization with a quantitative, efficiency-driven decision mechanism. The strategy is evaluated against an uncoupled baseline (REF-0) and a temperature-threshold-based predecessor strategy (REF-1) across a representative commuter cycle at ambient temperatures of −10 °C, 0 °C, and +30 °C using a co-simulation environment comprising a 1D ring-circuit fluid model in AVL Cruise M and a backward-facing 0D drivetrain model in MATLAB/Simulink. The results demonstrate measurable improvements in battery preconditioning and system efficiency at cold and moderate ambient temperatures. The heat-quantity-based strategy achieves comparable or superior thermal outcomes to the threshold-based approach while activating ring-circuit coupling more selectively. At warm ambient conditions, the strategy correctly withholds intervention based on a negative efficiency delta evaluation, confirming robust scenario-adaptive behavior. The findings highlight the potential of efficiency-driven coupling logic as a generalized and physically grounded basis for thermal operating strategy development in electrified powertrains.
Stenger, Erik, Fiore, Luis, Weimer, Niko, Beidl, Christian
Long-Stroke Air-Brake Actuator MarkingJ1817_202609 (Current)9/21/2026
This SAE Recommended Practice describes a marking system to distinguish long-stroke from standard stroke for service, parking, and combination air-brake actuators and components. Said actuators are used for applying cam-type foundation brakes by slack adjuster means.
Truck and Bus Brake Actuator Committee
A Study on TSC (Technical Safety Concept) Design and Verification in EMB (Brake by Wire) Systems2026-01-08019/14/2026
The Electro-Mechanical Brake (EMB) system is a dry-type Brake-by-Wire technology that eliminates hydraulic components and directly controls friction braking using electrical actuators at each wheel. The EMB architecture consists of a Main Center Control Unit, a redundant Backup Center Control Unit, and four Wheel Control Units communicating via CAN FD. Due to its direct involvement in vehicle braking, compliance with ISO 26262 functional safety requirements is critical. As system complexity increases, potential risks such as hardware failures and communication faults must be systematically addressed. The proposed TSC was developed according to ISO 26262, covering the concept phase (Part 3), system-level development (Part 4), and software implementation (Part 6). Safety goals and Functional Safety Requirements derived from HARA are used to guide system architecture design and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe operation. Verification is conducted at both system and vehicle levels using ECU-in-the-Loop Simulation (EILS), Hardware-in-the-Loop Simulation (HILS), and real-vehicle tests. Fault scenarios, including Main Center Control Unit failures and CAN communication losses, are injected using a custom LabVIEW-based fault injection tool. The study evaluates Fault Tolerant Time Interval (FTTI) settings, error handling mechanisms, and control handover strategies under fault conditions. The results show that redundancy and localized communication enable stable operation and smooth control transfer within the FTTI window without noticeable impact on braking performance or driver awareness. This study demonstrates the robustness of the proposed EMB architecture. Future work will focus on prognostics and maintenance strategies to support safe deployment in autonomous and electric vehicles. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Kim, Dokun
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-08069/14/2026
Recently, there has been a drastic shift in the industry towards wire architectures like steer-by-wire and brake-by-wire. For safe and accurate force control, diagnostics, and consistent performance over the operating envelope, accurate plant modeling of the Electro-Mechanical Brake (EMB) is important. Classical approaches involved linearized dynamic EMB models and the use of the characteristic stiffness curve for calibration at the operating points. These methods often perform poorly over regions where hysteresis, compliance, and friction are strongly nonlinear. Prior research on state or force estimation for EMB has focused on pad contact detection, thermal adaptation, and hysteresis-aware clamp force estimation. However, there are still accuracy gaps in practical applications during transients and under shifting friction regimes. In this work, a digital twin based on Physics-Informed Machine Learning is introduced, following the governing dynamics of the actuator-caliper assembly of EMB while learning (i) a physically significant parameter—system damping (Bsys) and (ii) a non-linear friction term constrained as a function of the actuator motion states and operating conditions. Non-linear friction is captured through gray-box friction formulation and learning unmodeled residual dynamics such as hysteresis and backlash. An EMB test stand is used to collect steps, ramps, holds/engagements, APRBS, and swept-sine excitations, with signals including time-aligned force command, motor torque/current, actuator position/velocity, and pad force measurement from a force sensor for model training. Results demonstrate a decrease in pad-force prediction error, along with non-linear and residual friction estimation. The resulting digital twin can enable sensor-less force estimation, friction compensation design, predictive analytics, and health monitoring through tracking parameter drift and friction signatures.
Rai, Prakhar, Gadhvi, Tirth
Architecting Safety-Critical Systems for AI-Enabled Software-Defined Vehicles: A Compute, Timing, and Isolation Framework with a Brake System Case Study2026-01-08119/14/2026
Software-defined vehicle (SDV) platforms are reshaping safety-critical system design by consolidating braking and other motion-control functions on centralized heterogeneous edge compute that also executes physical-AI workloads. This consolidation breaks traditional assumptions of fixed ECUs and simple timing envelopes, complicating assurance of determinism, isolation, and fail-operational behaviour for ASIL-D brake functions. Building on a decentralized brake-by- wire (BbW) architecture with dual controllers, redundant low-voltage power grids, and smart electromechanical brake corner actuators, this paper proposes a systems-level framework for architecting safety-critical functions in AI-enabled SDVs along three dimensions: compute, timing, and isolation. The framework classifies conventional and AI-based functions and maps them to heterogeneous compute classes; defines architectural patterns that combine safety islands, power-domain redundancy, and hardware partitioning to support freedom from interference; and formalizes timing domains and contracts that bound latency, jitter, and failover dynamics across sensors, centralized controllers, and decentralized actuators. The contribution is not a new AI algorithm, but a safety-oriented architectural framework that constrains how AI-enabled functions may be integrated into fail-operational by-wire systems. A BbW case study with edge-resident AI observers and anomaly detectors shows how the framework complements System Analysis Tool (SAT)– based failure modelling and clarifies trade-offs among safety isolation, latency, and AI performance while preserving braking safety guarantees under continuous software evolution.
Srinivasaraghavan, Soumyasudharsan
Real-Time Estimation of Brake Temperature using Physics-Informed Machine Learning Model2026-01-08109/14/2026
The current work presents a novel approach to estimating brake surface temperature in real-time to aid in brake wear prognostics. Brake prognostics involve estimating brake pad wear in real-time, which enables its predictive maintenance. Brakes are a safety-critical system for vehicles; therefore, they require accurate and robust pad wear estimation to ensure vehicle safety. However, it involves several challenges. The estimation of pad wear is fundamentally a two-stage process: the first stage involves the accurate prediction of brake pad surface temperature, while the second stage utilizes this thermal history to calculate cumulative material wear. A significant challenge in estimating brake pad wear without an expensive sensor is that it is sensitive to the surface temperature prediction; any error in the thermal model propagates and compounds in the wear prediction stage. To identify surface temperature, traditional physical sensors are often cost-prohibitive or prone to failure in the harsh thermal and mechanical environments of the wheel end, necessitating a robust virtual sensing solution that can capture complex, non-linear heat transfer dynamics. The current work addresses the above challenge of identifying temperature dynamics using a Physics-informed Machine Learning approach. We employ Symbolic Regression (SR), a data-driven method that discovers the underlying mathematical expression of the system dynamics by searching for the optimal functional relationship between variables. SR provides an interpretable model that can be generalized across automotive platforms, offering a transparent, computationally efficient, and analytically tractable alternative to traditional ‘black box’ models. To generate the temperature dataset, a test vehicles were equipped with thermal sensors and underwent various braking scenarios. The SR-based virtual sensing model demonstrated strong and consistent predictive fidelity across all braking conditions tested. Under mild braking scenarios, the model achieved a Mean Absolute Percentage Error (MAPE) of approximately 6.0% in predicting brake surface temperature. This performance remained highly robust under mixed and harsh, high-speed braking, the most thermally demanding scenario, yielding MAPEs of only 11.6% and 11.9%, respectively.. Across all regimes, this level of temperature estimation fidelity directly limits error propagation into the downstream brake pad wear prediction stage, enabling reliable, sensor-less, cloud-based brake health monitoring at scale.
Gannavarapu, Shivadath, Pal, Anuj, Fan, Mengdi
New Nonlinear Buckling Solutions of Piezoelectric Truncated Conical Shells2026-99-12629/4/2026
Piezoelectric materials are highly valued in engineering for their electromechanical coupling. With these characteristics, structural applications utilizing such materials are increasingly being employed across a variety of disciplines. Among these structural configurations, piezoelectric conical shells have garnered significant interest owing to their inherent electromechanical coupling behavior, making them ideal for applications in various devices such as actuation systems, sensing mechanisms and energy harvesting solutions. To ensure the structural safety of these devices, assessing the stability of such shell structures is essential. This study conducts an analysis of the buckling stability of truncated piezoelectric conical shells. To this end, a theoretical buckling model for piezoelectric truncated conical shells is established, based on first-order shear deformation theory combined with nonlinear pre-buckling deformations. Utilizing a novel set of displacement trial functions within the Galerkin framework, this study derives precise critical buckling loads along with their associated mode shapes. The accuracy of the model is verified through comparative studies in the numerical section. Subsequently, the influence of key parameters—including applied voltages, semi-apex angles, and shell thickness—on the buckling behavior is investigated. The findings indicate that including the nonlinear pre-buckling deformation in the analysis is essential for ensuring reliable predictions. This research offers a theoretical foundation for the dependable design and assessment of piezoelectric truncated conical shells. Moreover, they also create opportunities for smart structures in aerospace, civil, and robotics, where accurate predictions of stability under electromechanical loading are critical.
Zhang, Junlin, Chen, Lide, Jia, Jufang, Zhou, Zhenhuan
Study on the Effect of NaNO 3 +NaClO 3 Electrolyte on the Corrosion Resistance of the Oxide Layer on M50 Aviation Bearing Raceway2026-99-12839/4/2026
In the electrochemical machining (ECM) process of the M50 bearing raceway, the oxide layer’s corrosion resistance exerts a notable impact on the machining efficiency. To achieve high-efficiency ECM of M50 bearing raceways, the electrochemical impedance spectroscopy (EIS) testing technique was adopted to conduct systematic research on the anti-corrosion performance of the oxide layer on M50 bearing raceways under different ECM processing parameters (polarization voltage, polarization time, inter-electrode gap). Then, the impact of different processing parameters on the oxide layer’s corrosion resistance was revealed. The results show that the corrosion resistance of the oxide layer decreases with the increase of the polarization voltage and polarization time, and increases with the increase of the interelectrode gap. Based on this law, in the actual ECM process, the rapid formation of oxide layer can be promoted by adjusting the polarization time, increasing the polarization voltage and reducing the interelectrode gap, and finally, the efficiency of ECM can be improved.
Wu, Jianxing
Design and Validation of a 7-DoF Robotic Arm Featuring Hybrid Pneumatic-Electric Actuation2026-99-12229/4/2026
To enable more natural motion mapping between the human arm and a robotic counterpart while reducing control complexity, this paper presents a novel seven-degree-of-freedom (7-DoF) bionic robotic arm with hybrid pneumatic–electric actuation in an antagonistic configuration inspired by the skeletal structure and muscular actuation of the human upper limb. The design combines the high power density and intrinsic compliance of pneumatic artificial muscles with the precision and stability of electric motors, improving motion adaptability and payload-to-weight performance. Kinematic feasibility and motion smoothness for human-like waving are validated via forward kinematics and redundancy-resolved inverse kinematics, together with trajectory simulations. To quantitatively evaluate dexterity and operational range, Monte Carlo sampling is used to generate reachable postures across the workspace, producing a wrist activity map that characterizes attainable orientations and maneuverability. A prototype testbed is built to verify physical performance. Joint-angle tracking experiments for the wrist and elbow, as well as whole-arm coordinated-motion tests, demonstrate accurate trajectory tracking, smooth transitions, and stable motion. These results confirm the mechanical soundness and effectiveness of the proposed hybrid antagonistic actuation scheme. This work provides a practical basis for advanced control development and offers insights into hybrid actuation design for bionic robotic systems.
Dai, Yuanquan, Guo, Zhiqin, Zi, Mingkang, He, Zhaoyang, Song, Yongwei, Xie, Yinhui, Li, Jun
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