Browse Topic: Control systems

Items (5,850)
This article focuses on a wide range of high-precision storage and supply systems. Under the rated flow rate of 2.928 mg / s of the proportional flow controller, the instantaneous flow fluctuation range of the BangBang valve reaches 2.963 mg / s, exceeding the control accuracy requirement of 1% for the proportional flow controller. By establishing mathematical models of the BangBang valve, proportional valve, and proportional flow controller for simulation analysis, the trend of the simulation results is consistent with the experimental results. Furthermore, considering the spatial layout and weight of the storage and supply system, this paper proposes a method to improve the accuracy of flow output by adding 180 mL of air capacity between the proportional valve and the proportional flow controller. Ultimately, the maximum flow fluctuation of the proportional flow controller at the moment of the BangBang valve opening and closing is 2.941 mg / s, which meets the control accuracy of the
Li, ZhongYan, ZelongHuang, Tiankun
In order to achieve precise control of refueling volume, improve oil change efficiency, reduce oil pollution and waste, a new oil change device for the reducer of the range hood equipment is studied. We design a new oil change device that integrates oil discharge and refueling functions based on the operating characteristics of the reducer in the range hood equipment. Using the rotational speed of the power pump and the flow rate of the oil pipeline as variables, we determine the refueling flow rate using a one-dimensional quadratic formula. Based on direct control theory, we optimize the relative position parameters of each component of the device, establish a control matrix, and achieve precise control. The experimental results show that the new oil change device exhibits good performance during both one-time oil discharge and refueling processes, meeting the precise control standards for refueling volume. The design and application of a new oil change device can effectively improve
He, PengtaoWei, BoLiang, ZhiyuanDeng, WeirenLiang, WenbinXing, Yuquan
To address the challenges faced by micro flapping-wing flying robots in visual navigation—specifically, the large volume of visual information and the difficulty in transforming it into usable intelligent visual data—this paper proposes a clustering-based data-driven approach for directional and image perception. The aim is to enable intelligent visual navigation for flapping-wing robots. The proposed method performs clustering analysis on gyroscope data from the flapping-wing robot to extract directional features. Simultaneously, it applies clustering techniques to visual images captured by the robot to identify intelligent features such as edges. This approach enables the robot to acquire multiple optimized perceptual data types, thereby enhancing the behavior control system. Through the use of clustering analysis, the method not only improves the effectiveness of visual navigation but also extracts features related to visual targets and environmental information, providing technical
Li, ZixuanDing, WeiZhang, FengSong, MinLiu, ZhaomingMiao, LeiLiu, HaotianBai, NingTian, ShenCui, LongWang, Hongwei
Quadrotors (UAVs) are widely used in intelligent inspection, environmental monitoring, and logistics due to their simple structure, strong maneuverability, and vertical take-off and landing capabilities. However, their highly nonlinear, strongly coupled, and highly constrained dynamic characteristics make trajectory tracking control a challenging task. To improve trajectory tracking accuracy and control robustness, this paper proposes a quadrotor trajectory tracking method based on model predictive control (MPC). First, a six-degree-of-freedom dynamic model of the quadrotor is established and linearized with small disturbances to transform it into a state-space model suitable for MPC design. An MPC optimization controller is then constructed, with an objective function that minimizes state error and imposes an input energy penalty, while explicitly considering the system's input and state constraints. Simulation results demonstrate that this method exhibits good tracking accuracy and
Peng, FeiTao, ZhongGao, QiangJia, Bobo
The development of remote tower systems in aviation and the resurgence of multi-display interfaces and virtual environments have dramatically influenced ATC, increasing both controllers’ visual demands and their ergonomic needs. This study uses the Visual Ergonomics to study the impact of screen luminance level, along with color temperature, on trainees’ visual performance, fatigue, and physical discomfort in the control rooms of the Remote Tower. By combining a simulated remote control system with spectrometer measurements, PVT alertness tests, VMT (Visual Memory Test) measurements, and subjective evaluations, COST B21 can build up a multi-dimensional ergonomic assessment framework. Eight levels of display luminance (and color temperature) were tested, including two illuminance levels (300 lx and 400 lx) and four color temperature ranges (6000 K–9000 K). Using the Analytic Hierarchy Process (AHP), these parameters were assigned weights to derive a Visual Ergonomics (VE) scoring model
Zhong, LinfengHu, RuohuiLuo, PeilinZuo, QinghaiZhong, QingweiAi, Yi
This study presents a full-envelope attitude-stabilisation and trajectory-tracking strategy for morphing flying-wing UAVs operating in highly nonlinear and strongly coupled conditions. The approach integrates fuzzy C-means (FCM) envelope partitioning with L1 adaptive control. Small-disturbance linear models are first generated at multiple altitude–Mach trim points; the FCM algorithm then performs unsupervised clustering in the state space, yielding representative subintervals that capture local flight-dynamic characteristics. The optimal cluster number and fuzziness exponent are selected using the partition coefficient, partition index, partition entropy, and Xie–Beni indices. For each sub-interval, an LQR baseline controller is designed and augmented by an L1 adaptive compensator, where a low-pass filter decouples adaptation from robustness to guarantee specified transient-performance bounds under matched/unmatched uncertainties, actuator saturation, and external disturbances. A feed
Tang, LonghaoSun, XiaoxuLiu, Changlin
With the rapid development of the low-altitude economy—represented by drone logistics, aerial inspections, and air taxis—air traffic has exhibited new characteristics including diverse forms, high density, and significant speed differences. To address these changes, the traditional air traffic control system requires upgrades, particularly in dynamic aircraft scheduling. This study proposes an air traffic control model (DS-ATM) tailored to this domain, built on the Deepseek large model. By integrating spatiotemporal graph neural networks with multi-objective reinforcement learning algorithms, the model achieves real-time path planning and conflict resolution in complex airspace environments. Validated using public datasets such as OpenSky Network, NASA UTM Dataset, and METAR meteorological data, experimental results demonstrate its significant advantages in reducing conflict rates and scheduling delays.
Li, RuiZhao, FangyuShe, YueLi, Wujie
Batteries generate a large amount of heat during operation, and if it cannot be dissipated in a timely and effective manner, it will seriously affect the performance, lifespan, and even safety of the battery. Therefore, battery heat dissipation has become a key challenge in the development of new energy vehicles. The traditional liquid cooling system has problems such as complex design and control, and the need to improve heat dissipation efficiency. To address these issues, this study proposes an optimized design scheme for battery environment heat dissipation control system based on liquid cooling heat dissipation system. This study first conducted an in-depth analysis of the thermal generation mechanism of lithium-ion batteries and studied existing examples of thermal management schemes. On this basis, an innovative forward and reverse circulation device was designed, combined with a liquid cooling heat dissipation structure. The Keil uVision4 programming software was used to write
Ding, XvqiangNi, YiweiGu, ChenZhang, JinChen, MingyangJiao, Yunxiao
The turbine hybrid electric propulsion system is an important form of green aviation. Unlike the single form of aviation power scheme, the hybrid energy system is flexible in architecture, uses two or more energy forms, and has diverse energy sources. Under different mission requirements, it needs to meet the requirements of mass balance, energy balance, and power demand, etc. Therefore, The control and distribution management between different energy systems have become the key to hybrid power, and power management technology is one of the key challenges in the development of aviation hybrid power control systems. This paper reviews the current structural forms of aviation turbine hybrid electric propulsion systems, analyzes the current research status of power management technology for aviation hybrid systems, and points out that the online power management method based on optimization is the best power management technology solution for turbine hybrid electric propulsion systems
Cai, ChangpengLiu, HaoGu, JiangweiLi, ShunmingZhang, Haibo
Aiming at the problems of model uncertainty, external disturbances and high-frequency chattering of traditional sliding mode control in complex working conditions for quadrotor unmanned aerial vehicles, this paper proposes a control strategy based on fractional-order sliding mode. The quadrotor UAV control system has problems such as parameter uncertainty, multi-input multi-output, and sensitivity to internal and external disturbances. Traditional PID control has certain limitations. Sliding mode control has the advantages of strong robustness and simple implementation. Fractional-order calculus has hereditary and memory properties. The combination of the two has better control performance for nonlinear systems. To further improve the trajectory tracking performance of quadrotor UAVs, a fractional-order sliding mode controller is designed based on fractional-order theory and traditional sliding mode control. Finally, multiple experiments are conducted in Matlab/Simulink, including
Liu, JingyiZhou, QiWang, JiajiaLu, Zhaona
In recent years, with the low-altitude economy developing rapidly, the operation and management of low-altitude airspace has gradually become a hot topic. Unmanned aerial vehicles (UAVs) constitute a fundamental component of the low-altitude airspace ecosystem, significantly influencing its structure and functionality. The technological advancement of UAVs has fundamentally transformed the operational paradigm for low-altitude airspace management. This paper presents a comprehensive review of UAV-supported technologies in the context of low-altitude airspace operations and management. It systematically analyzes key technologies and applications of UAVs in areas such as airspace capacity and safety assessment, trajectory planning, and standardized flight management. Drawing from kinematic analysis and traffic flow theory, UAV density control and collision risk prediction offer quantitative insights into airspace capacity evaluation. Additionally, probabilistic analysis and simulation
Gong, LeiMa, ZhenxiaoLuo, Qin
Trajectory tracking control and vehicle state estimation are core functionalities of highly automated vehicles and must operate reliably under strict real-time constraints as well as in the presence of model uncertainties and limited sensor availability. This paper presents an integrated, real-time capable framework for trajectory tracking control and vehicle state estimation, developed within the UShift II research project and implemented on the highly automated vehicle platform. The framework combines nonlinear model predictive control (NMPC) for trajectory tracking with an extended Kalman filter (EKF) for multi-sensor state estimation within a modular system architecture. The NMPC is based on a vehicle model designed for low-speed automated driving maneuvers and explicitly accounts for actuator constraints. Trajectories are tracked based on local planned reference trajectories while ensuring smooth and physically feasible control inputs for underlying control. The EKF fuses
Fuchs, SörenNeubeck, JensWagner, Andreas
Opposed-piston free-piston engine generators (OFPEGs) are emerging as a promising technology for next-generation hybrid and electrified transportation systems due to their high efficiency, reduced mechanical complexity, and improved noise, vibration, and harshness (NVH) characteristics. However, due to eliminating the conventional crankshaft mechanism and directly coupling a free-piston engine with linear generators, performance of OFPEG systems is governed by a strong coupling between piston dynamics, in-cylinder combustion processes, and electrical loading conditions. This coupling presents substantial challenges for system design, control, and optimization, limiting the further development and application of OFPEGs. Existing researches lack a comprehensive numerical model that integrates detailed in-cylinder thermodynamic process with control system of linear generator, and quantitative analysis of the effect of piston motion trajectory on system performance remains insufficiently
Wang, JiayuMorandi, NicolaLucchini, TommasoFENG, HUIHUAJia, BoruRen, Peirong
Distributed drive electric vehicles (DDEVs) provide enhanced maneuverability through independent wheel torque control, but coordinating precise path tracking with lateral stability remains challenging under aggressive driving conditions. This paper presents a coordinated control strategy that integrates model predictive control (MPC) for path tracking with a proportional gain controller for stability regulation. The proposed framework adopts a hierarchical design. The path tracking control leverages MPC to compute front steering commands while accounting for vehicle dynamics and preview errors. The stability adjustment uses dual proportional gain controllers to generate an additional yaw moment, which is adaptively balanced through a phase plane coordination mechanism, enhancing yaw stability during path tracking. The generated yaw moment is subsequently distributed to individual in-wheel motors with an optimization torque allocation method, respecting tire force limitations. The
He, YangZhu, YuzhengGuo, RuixinZhu, YueyingXing, ChaoLiu, ShuangxiLin, Yier
OEMs, integrators and suppliers must continuously process, assess and identify platform vulnerabilities to prioritize and implement updates that protect systems from cyberattacks and data breaches. Security researchers have demonstrated that the control systems in vehicles and machines are open to attack. In 2010, researchers from the University of Washington and the University of California, San Diego demonstrated that by gaining physical access to a vehicle, they could manipulate critical systems like brakes and engines. Just a few years later, security researchers Charlie Miller and Chris Valasek remotely compromised a vehicle over the internet, controlling steering, braking and acceleration, leading to a 1.4 million vehicle recall. In 2024, researchers at Colorado State University successfully demonstrated a wireless drive-by hack by exploiting vulnerabilities in common electronic logging devices (ELDs). In their proof-of-concept test, they achieved remote control over a truck by
McGuirk, Finn
Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. This paper introduces an advanced Electric Health-Monitoring Wiring (E-Wiring) system that integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based
Tammana, Bala Sai Sri RohitMurthy, HarshaMendu, HarikaSivaniSunandha
This paper investigates the energy consumption characteristics of series hybrid aircraft with a focus on comparing conventional energy management approaches against an AI-powered optimization framework. The study comprehensively models the energy demands of a series hybrid aircraft across all major flight phases, including Idle & Ground Operations, Taxi, Takeoff, Climb, Cruise, Descent, Approach, Landing, and Rollout & Taxi. For each phase, detailed mathematical formulations are developed to capture power requirements and energy flow, incorporating real-time operational parameters to enhance the accuracy of the energy consumption estimations measured in kilowatt-hours (kWh). The AI-based optimization leverages advanced control strategies, specifically Model Predictive Control (MPC) and Reinforcement Learning (RL) algorithms, to dynamically manage the aircraft’s energy systems. MPC is employed to predict and optimize future energy usage by solving constrained optimization problems over
Kanchagar, Amogha
This paper addresses the critical challenge of fault-tolerant control in autonomous multi-copters, particularly under conditions of one or two rotor failures a scenario that often leads to severe instability and a complete loss of directional control due to unbalanced torque and resultant autorotation. Existing advanced control strategies, including optimal approaches such as LQR, typically require precise system modeling and state estimation, which are difficult to achieve in real-world, dynamic failure scenarios. Alternative methods like fuzzy logic, sliding mode control, and gain-scheduling either lack robust generalization or are impractical for enumerating all possible failure cases. In this work, a hybrid control framework integrating Physics Informed Neural Networks (PINN) with a standard PID controller is proposed for fault-tolerant operation of autonomous multi-copters subject to multiple actuator failures. PINNs incorporate governing physical laws as regularization in their
Charapalle, SamruddhiVenugopalan, NandagopalanNerkundram Muralidharan, ArunSundararaj, Laveen
Unmanned Aircraft Systems (UAS) are increasingly deployed in diverse missions, and maintaining heading stability in the presence of unpredictable wind disturbance is a significant challenge. This paper proposes a novel model reference adaptive gain-scheduled PID (Proportional-Integral-Derivative) control framework tailored for the heading control of flapping-wing UAS (ornithopter) operating under dynamic wind conditions. The control architecture integrates an estimated wind disturbance value and adaptively tunes the PID gains by minimizing the error between the actual system response and a desired reference model. Gain scheduling mechanism uses airspeed, yaw rate, and estimated wind magnitude to ensure stability. The proposed method is validated on a 6-DOF UAS simulation model subjected to dynamic wind and temperature variation profiles. Comparative results show improved heading accuracy, responsiveness, and robustness over conventional fixed-gain and static gain-scheduled PID
M V, ArunaMelissa, Arul
The electrical harness system of satellite launch vehicles functions as the backbone of spacecraft avionics; inter connecting subsystems through complex networks of wires and connectors. An electrical harness is a group of wires bunched together and terminated in connectors. The common insulations used for launch vehicle applications include PTFE, Polyimide, ETFE and TKT. The connectors used are of aerospace grade and connectors tailored for space applications. With over 5000 connectors and 200 km of cables constituting nearly 20% of vehicle mass, the design, fabrication, and sustainability of these systems are critical. The insulations of connectors inserts or the wires are critical for the durability of harness elements. Nevertheless, these insulations are non-expendable and pose disposal challenges and some releases toxic gases when burned or due to vacuum outgassing phenomenon. Also, the cadmium plating which is often used for the environmental resistance of connector shells
K S, NithishTR, BinnyD S, Praveen Kumar
The aviation industry represents a significant greenhouse gas emitter and aims to reduce net CO2 emissions to zero by 2050. The deployment of sustainable aviation fuel (SAF), alongside measures such as increasing engine efficiency and enhancing ground handling processes, represents a key driver to reach this ambitious goal. SAF exhibits significantly different physical and chemical properties compared to conventional kerosene. The corresponding fuel specification (ASTM D7566 [1]) currently only defines fuel parameters relevant for the use in jet engines. To assess the suitability of SAF for the use in compression ignition (CI) aviation engines, a collaborative project was conducted at TU Wien—Institute of Powertrain and Automotive Technology, together with Austro Engine. ASTM D7566-certified fuels like Hydrotreated Vegetable Oil (HVO), Fischer–Tropsch–Kerosene (FTK), and Alcohol-to-Jet (AtJ) have been investigated on the engine test bench at TU Wien. The core contribution of this study
Kleissner, FlorianHofmann, Peter
Based on the multi-objective hierarchical optimization solution method, this paper takes both system balance and scheduling economy into account, and constructs a hierarchical collaborative optimization model for the multi-energy complementary system of offshore energy islands. To address the impact of the volatility and randomness of offshore wind farm clusters on the scheduling of energy island systems, the Stochastic Model Predictive Control (SMPC) method is adopted to optimize and solve the scheduling of offshore energy islands. This paper innovatively proposes a scheduling method based on adaptive variable-step stochastic model predictive control. In the rolling optimization process of SMPC, this method tracks the real-time scheduling deviation degree through the deviation reference coefficient and changes the rolling optimization step size. It solves the problems of insufficient scheduling accuracy and being trapped in local optimization in the rolling optimization process of the
Huang, HaochengZhang, JinqiZhou, FengfengYan, QihuiXu, ChangYin, Gaojun
Autonomous Vehicles (AVs) offer unprecedented opportunities to design control strategies that could be able to simultaneously enhance safety, performance, user experience, time efficiency, and the environmental impact of mobility. However, as automation levels increase, a paradigm shift becomes not only necessary but imperative: the integration of human needs into mobility objectives. This includes not only traditional comfort considerations but also minimizing Motion Sickness (MS), a largely under-explored challenge in control strategy design. In recent literature, several methodologies for modeling and mitigating MS have been proposed, yet their integration into vehicle control logics remains limited, often restricted to isolated and specific case studies, with the research area largely unexplored, particularly with respect to the generalization of the proposed methods. This work introduces a theoretically grounded multi-objective Nonlinear Model Predictive Control (NMPC) framework
Ponticelli, LorenzoBottiglione, FrancescoRini, GabrieleTimpone, FrancescoSakhnevych, Aleksandr
Indoor thermal comfort is closely related to people’s health and work efficiency. Control systems typically consume a large amount of energy to maintain a comfortable thermal environment. Currently, reinforcement learning is widely applied to optimize thermal comfort control systems. However, existing research mainly adopts universal thermal comfort evaluation models that aim to satisfy the majority of people, which makes it difficult to quickly and accurately reflect the specific thermal comfort needs of individuals. As a result, the hot environment is neither comfortable nor energy-efficient in practical use. Therefore, this paper proposes an energy-saving personalized thermal comfort control method based on decision trees and reinforcement learning. First, decision tree learning is used to obtain an individual thermal comfort evaluation model from a small amount of historical data. Then, this individual comfort model is combined with energy consumption to form a reward function
Li, Xianying
To improve the handling stability of four-wheel steering/drive vehicles under complex high-speed maneuvers, this study proposes a coordinated control strategy that incorporates Active Rear Steering (ARS) and Direct Yaw Moment Control (DYC) based on a dynamic stability region. Firstly, a four-wheel steering vehicle dynamics model including lateral motion and yaw motion is established, and the ideal values of the control variables are determined. Secondly, combined with the fuzzy control theory and double-line method, the boundary of the dynamic stability region is obtained in the sideslip angle-sideslip angle rate β−β̇ phase plane, and the vehicle state is categorized into stable, unstable, and critical stable region. Then, A hierarchical control architecture is designed based on the stability boundary. The upper controller comprehensively solves the target rear wheel angle and additional yaw moment through feedforward feedback control; the coordinated control layer allocates control
Nie, KeheChen, JinWang, FalongLi, RenBai, Xianxu
The suspension system with variable damping and variable stiffness actuators can realize four-quadrant mechanical output, effectively combining the energy efficiency of the semi-active suspension with the performance levels approaching those of active suspensions. However, the practical effectiveness of this system depends heavily on the ability of the control strategy to adapt to different driving conditions. In order to meet this challenge, this research has developed a multi-mode suspension collaborative control strategy to optimize energy efficiency and ride comfort in various operating scenarios. Based on the four-quadrant characteristics of the actuator, a suspension mode switching framework has been established, and the suspension work is divided into passive, semi-active, pseudo-active and active modes. In order to determine the appropriate switching boundary, first calculate the root mean square (RMS) value of the sprung mass acceleration and suspension dynamic deflection
Li, ZhiyingLi, JeiZhu, AndingBai, XianxuLi, WeihanLi, Rui
To address the issues of significant slip energy dissipation induced by severe tire slip, degradation of vehicle control stability, and insufficient accuracy of vehicle speed tracking under low-adhesion road conditions, a torque coordination control strategy for dual-motor electric vehicles (DM-EVs) considering load transfer and slip energy dissipation is proposed. First, a vehicle dynamics model integrating suspension system dynamics and tire slip characteristics is developed, fully accounting for the influence of front-rear axle load transfer on the tire slip ratio. Next, founded on the energy dissipation mechanism of tire slip, a quantitative model for energy dissipation during tire slip is developed. Finally, a longitudinal coordinated control system for vehicles according to nonlinear model predictive control (NMPC) is introduced. By comprehensively considering the tire slip ratio and vehicle load distribution, multi-objective coordinated optimization of wheel torque is achieved
Hou, YingmingLi, JieBai, Xianxu
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm
Yu, JingzeWang, YujiaLi, JunshenChen, CongXu, Peng
The aging of the population has been a key issue worldwide, with mobility and fall of the elderly an important problem to be solved. In this paper, we propose an elderly mobility assist system based on the intelligent power-assisted device consisting of an assistive cane and an intelligent companion. It has the functions of standing support after falling, daily support and on-site rest. The assistive cane adopts a two-stage expansion mechanism of crank and slider structure, which forms a stable triangular support after unfolding, so that the patient can stand safely. The intelligent companion platform is driven by drive wheels, equipped with pushrod motors and vacuum suction devices, it can automatically approach the user and form an stable support column when the cane is in the out-of reach range; the control system is designed by combining microcontroller, camera object recognition, wristband remote control, to realize automatic steering and autonomous navigation at differential
Yu, ChenxiWang, LongyiZhu, HuayunDong, YanMi, RuixueZhu, Lihong
In China, the installed capacity of renewable energy sources such as wind and photovoltaic power has ranked first in the world for consecutive years, and new energy has become a core driver of energy structure transition. However, the strong volatility and intermittency of new energy output seriously affect the safe and stable operation of the power system, and high-efficiency energy storage technology is the key to solving this problem. Focusing on the short-term high-power charging and discharging characteristics of high-temperature superconducting magnets (SMES), this study proposes a Hybrid Energy Storage System (HESS) that combines SMES with Battery Energy Storage Systems (BESS) to enhance the short-term power support capability of electrochemical energy storage. Variational Mode Decomposition (VMD) is introduced to establish a multi-level power allocation method, which addressing issues such as mode mixing, end effects, and low decomposition efficiency that are prone to occur in
Liu, HaiyangWang, PengfeiZhou, WenLu, JingWu, YananYin, YunkuoJiang, Liping
The stable operation of islanded DC microgrids is conditioned by two essential objectives. One is to maintain the bus voltage at its nominal value, and this can ensure system stability. The other is to achieve cost-effective power allocation among distributed generation units, which guarantees economic efficiency. These two objectives are often conflicting. Adding droop control to the voltage and current dual closed-loop control can achieve primary current sharing. However, it inevitably introduces steady-state voltage deviations on the DC bus and results in inflexible or not optimal power sharing. To resolve these inherent limitations, this paper proposes a innovative distributed secondary control strategy. The method is designed to meet both requirements within a unified framework. In the primary control layer, it uses adaptive droop gains to optimize power distribution in real time based on changing load requirements which enables distributed generation units to achieve cost
Sun, WeiShe, DunjunYu, JinzhuYuan, WeiboPeng, BoZheng, Yingchun
Surface electromyography (EMG) signals are essential for facilitating intuitive interactions between humans and bionic hands. However, their inherent non-stationarity, low signal-to-noise ratio, and significant inter-individual variability present considerable obstacles to precise decoding. To overcome these challenges, this study proposes a novel recognition framework combining wavelet packet decomposition and a dynamic graph convolutional-Transformer model. The process starts with multi-layer wavelet packet decomposition and adaptive threshold denoising, effectively removing noise while retaining critical signal features. Subsequently, a dynamic graph convolutional network is employed to capture spatial interactions among multi-channel electrodes, and a Transformer encoder models long-term temporal dependencies within the signals. By integrating these methods, the model generates a fused feature representation that incorporates both spatial and temporal correlations. Experimental
Huang, RuiZhao, YueYang, PenghuaZhu, JintaoXiong, Xibei
Autonomous vehicles exhibit extremely strong nonlinearity during drift. However, existing autonomous drift algorithms often neglect previewed path curvature and offer only limited consideration of road surface uncertainty because of the influence of vehicle nonlinear dynamics, which can affect tracking accuracy and robustness of drift control. To solve these problems, this study proposes a robust optimal drift control framework based on curvature preview. First, a preview vehicle kinematic model is constructed, and a preview model predictive control path-tracking controller that considers the forthcoming curvature is designed. Through the analysis of equilibrium points with additional yaw moment, a robust optimal drift controller is developed, which employs a three-degrees-of-freedom vehicle model with an additional yaw moment. This controller adopts integral sliding mode control with a super-twisting algorithm (STA) and exhibits good stability, which is verified through Lyapunov
Gan, YurunSong, ZiyuGu, TongtongDing, HaitaoXu, NanZhang, Jianwei
This organizational process survey provides insight into the technical aspects of approved airworthy aircraft modifications applied in government organization vertical lift flight test. The publication reviews processes applied by the National Research Council of Canada's Flight Research Laboratory (NRC-FRL) and its Airworthiness Control System to enable research flight testing. Dominated by the need for integrating experimental payloads, the NRC-FRL embeds a Design and Fabrication Service organization for modification of internal and external client projects and flight test aircraft. In context of experimental flight testing, this work reviews technical information on process, facilities, and methodology for airworthy integration of flight test payloads. Information is used to synthesize recommendations in experimental vertical lift flight testing that satisfy both formal (regulated compliance) and informal (compliance intent) airworthiness requirements.
Alexander, MarcLong, TerryLebrun, CamileDay, JamesMelnik, HarleyThomas, Jeffrey
This paper presents the development, optimization, and flight test validation of a Trajectory Control System (TCS)-based flight control system for a tiltwing unmanned aerial vehicle. The TCS is a configuration-independent middle-loop longitudinal controller for vertical takeoff and landing aircraft and is integrated here with explicit model following inner-loop controllers, inverse propulsor models, and a tiltwing-specific control allocation scheme. The resulting flight control system provides coordinated control across vertical flight mode, hybrid flight mode, transition flight mode, and forward flight mode while relying on a concise feedback set and requiring only airspeed from the air data system. The control laws are obtained using a formal constrained optimization framework and transferred directly from simulation to flight without additional on-site retuning. Flight test results from piloted, semi-autonomous, and fully autonomous operations demonstrate stable and predictable
Comer, AnthonyChakraborty, ImonKunwar, BikashSchmidt, Peter
Full state feedback offers theoretically guaranteed multi-axis stability, making it superior to conventional PID controllers. There is however one drawback, a full state controller has a mathematical difficulty if the B matrix is not square and thus not invertible. This is the case for helicopters with 6 degrees of freedom and 4 inceptors. Variations of linear quadratic regulators are a work around, however complexity dramatically increases. Best would be a direct solution to the original problem. This is the breakthrough result of this paper. This paper documents an approach which removes the analysis roadblock by partitioning the 6 x 6 system "A" matrix into two groups of 4 x 4 matrices. The 4x4 matrices are individually stabilized with full state gain matrices. One matrix is designated “Driver Matrix” which provides actuator commands. The other matrix is designated "Reference Matrix" which provides references. The two matrices are coupled together by requiring that the driver matrix
Mouritsen, StephenPiasecki, Fred
Atmospheric turbulence is a major source of uncertainty for unmanned rotorcraft operating in confined or disturbed environments, where robust trajectory planning requires reliable bounds on vehicle response. High-fidelity turbulence models are typically too computationally demanding for onboard use and difficult to integrate into planning frameworks. This paper presents a Control Equivalent Turbulence Input (CETI)–based approach to characterize turbulence effects on the inner-loop dynamics of a small unmanned helicopter and to derive disturbance-induced state deviation bounds suitable for robust planning. CETI models are identified from manually piloted hover flight tests of the unmanned research helicopter midiARTIS using a linear bare-airframe model and a Kalman filter for disturbance estimation. CETI transfer functions are fitted to averaged power spectral densities of the extracted disturbance inputs. The resulting model is validated by reproducing the identified transfer functions
Ehlert, TobiasSchitz, PhilippDux, Rafael
This work describes the flight control system architecture of the VSDDL VT-03-s Shadow, a cost-effective subscale aircraft used as a testbed for novel flight control schemes. The highlight is the Maneuver Control System comprising the Trajectory Control System, which facilitates Simplified Vehicle Operations, and the Tactical Maneuvering System, which permits more aggressive maneuvering. The control laws permit the selection of both vertical takeoff and landing and conventional takeoff and landing modes of operation. Flight test results shown include transitions between vertical and forward flight modes performed using both Trajectory Control System and Tactical Maneuvering System, limited aerobatic maneuvering performed using the Tactical Maneuvering System, and demonstration of some of the automatic flight functions and capabilities.
Chakraborty, ImonMcCormick, ColeKunwar, BikashBhandari, RajanPutra, Stefanus Harris
The Enhanced Tiltrotor blade, also known as the RGF3 blade, represents a major milestone in Leonardo Helicopters Division's pursuit of advanced rotorcraft technology. Developed at the Yeovil facility in the United Kingdom as part of a dedicated program and in collaboration with the European Clean Sky 2 initiative, it is a key enabler for the Next Generation Civil Tiltrotor Technology Demonstrator. Leveraging the AW609 airframe, the NGCTR integrates a new lateral rotor control system and a V-tail with ruddervators to expand maneuverability and control authority. The RGF3 blade combines aerodynamic efficiency with manufacturability, cost effectiveness, and certification readiness. Innovations include advanced airfoil families, highly swept anhedral tips, dual-redundant anti-ice systems, and full compatibility with legacy components. A comprehensive test campaign—covering structural loads, lightning and bird strikes, icing, and wind tunnel validation—confirmed its robustness and
Paoli, Michele DelliD'Andrea, Andrea
This paper presents an adaptive model predictive control (MPC) framework for nonlinear urban air mobility (UAM) vehicles operating across the full flight envelope. The proposed approach leverages a linear parameter-varying (LPV) representation to update the predictive model online, enabling accurate capture of strongly nonlinear and time-varying dynamics associated with distributed electric propulsion (DEP) eVTOL aircraft. To systematically address the highdimensional and coupled nature of MPC tuning, a multi-objective evolutionary optimization strategy based on NSGAII is employed, incorporating proper normalization of states and control inputs to ensure balanced weighting and meaningful exploration of the design space. The resulting controller explicitly accounts for actuator constraints and enables reconfigurable control allocation for fault-tolerant operation. The framework is evaluated in nonlinear simulations using NASA's Generic Urban Air Mobility (GUAM) model and benchmarked
Ngo, Tri
This paper presents the design and simulation-based evaluation of a configuration-independent Trajectory Control System (TCS) for multiple vertical takeoff and landing (VTOL) vehicles. The TCS provides a unified, middle-loop longitudinal control system applicable to lift-plus-cruise, tiltwing, and vectored-thrust configurations. Developed under the Simplified Vehicle Operations (SVO) paradigm, the TCS computes thrust-to-weight commands from normalized vertical and horizontal acceleration using inertial frame force-balance relationships and allocates the resulting trajectory requirements across the available propulsors. The governing TCS equations, propulsor-share framework, and mode structure remain common across configurations, while configuration-specific effects enter only through mode thresholds, inverse propulsor models, and control allocation. The control laws require only attitude, angular rate, fore-aft acceleration, and vertical velocity feedback. Subscale simulation
Comer, Anthony
To address the performance testing requirements of autonomous vehicles (AVs), this study proposes a model predictive control (MPC) algorithm specifically designed for low-ground-clearance test target vehicles (TTVs) to achieve trajectory tracking control. First, the kinematic model of the TTV is established, and its state-space equations are derived. An objective optimization function incorporating both error weighting and control weighting is designed. Simulation analysis reveals the influence of the control error weighting ratio (CEWR) on both straight-line and curved trajectory tracking performance: For straight-line tracking, increasing the CEWR from 10 to 25 reduces the overshoot, but increases the distance required to reach the target trajectory by 4.7%. A similar pattern is observed in curved trajectory tracking. To overcome the limitations of the fixed CEWR, an improved MPC algorithm integrating fuzzy control is proposed. This algorithm dynamically adjusts the CEWR in real time
Ji, ShaoboLu, YueqiLiao, GuoliangChen, ZhongyanLi, MengLyu, ChengjuZhang, Zhipeng
Electrified powertrains—such as Power Splits, Series Hybrids, and EVs with Disconnect Actuators—enable flexible management of actuator acceleration and torque from shared power sources. In power-limited or high-demand conditions, the Hybrid Supervisor must balance available power to sustain performance and drivability; poor coordination can cause control imbalance, reduced actuator performance, and unintended motion. Conventional methods often favor a single control objective, compromising overall system efficiency. This paper introduces FLAIR (Fuzzy Learning Adaptive Integral Response) Control, a supervisory strategy for actuator speed profiling and driver demand tracking in single-input multi-output (SIMO) systems. FLAIR integrates an integral of tracking error with fuzzy inferencing to dynamically weigh multiple control goals, adapting acceleration limits in real time while preserving driver power demand tracking. It enables bi-directional power-flow decisions—allocating system
Banuso, AbdulquadriSha, HangxingShenoy, AayushMadireddy, Krishna Chaitanya
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