Browse Topic: Fuzzy logic

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A Proposed Methodology for Human Operator Modeling and Development of a Virtual Operator Model for Control of a Simulated Compact Track Loader2026-01-0146To be published on 04/07/2026
A methodology for performing Human Operator Modeling (HOM) using a Caterpillar Model 299D3 XE Compact Track Loader (CTL) is presented. The proposed method uses task analysis techniques to decompose a material excavation and moving task into smaller, individual tasks presented in a task list. A method for verifying and refining the task list is presented, along with a procedure for identifying relevant human operator sensory information and analyzing human decision making in the context of CTL operation. This methodology is then partially verified through the analysis of a non-expert human operator in Vortex Studio, a realistic construction equipment simulator. A modified test course is executed by a non-expert human operator in the simulation environment, and the recorded data is used to create a quantitative Human Operator Model. From this, a Virtual Operator Model (VOM) feedback controller simulating the performance of the human operator is developed. The VOM is implemented using a
Wang, Orson R., Norris, William R., Patterson, Albert E., Soylemezoglu, Ahmet, Nottage, Dustin S.
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-0147To be published on 04/07/2026
The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive
Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-0148To be published on 04/07/2026
Offroad autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6x4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6x4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6x4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational
Dekhterman, Sam, Patterson, Albert, Sreenivas PhD, Ramavarapu, Norris, William, Soylemezoglu PhD, Ahmet, Nottage PhD, Dustin
Research on Intelligent Vehicle Fleet Longitudinal Control Based on Fuzzy Model Predictive Control2026-01-0040To be published on 04/07/2026
Addressing the challenge of balancing tracking efficiency and stability in longitudinal cooperative control of intelligent vehicle platoons, this paper proposes a hierarchical control strategy integrating fuzzy logic with Model Predictive Control (MPC). The upper-layer controller, based on the platoon's longitudinal dynamics model, formulates an optimization objective function that considers both tracking performance and ride comfort. A fuzzy logic system is introduced to adaptively adjust the weighting coefficients within the MPC objective function in real-time based on the prevailing tracking state, outputting an optimal desired acceleration. The lower-layer controller, utilizing an inverse longitudinal dynamics model incorporating the engine's universal characteristic map and a brake system model, accurately translates the desired acceleration into throttle opening or brake pressure via a Proportional Integral Derivative (PID) control algorithm. To validate the control strategy, a
Li, Ye
An Interpretable Deep Learning Decision-Making Framework for Autonomous Lane-Change Inspired by Human Driving Experiences2025-01-733912/31/2025
Lane change plays a critical role in autonomous driving and directly affects traffic safety and efficiency. Although deep learning-based lane-change decision-making frameworks have achieved promising results, they still face fundamental challenges in producing human-consistent and trustworthy behavior, mainly due to: 1) Inadequate psychology-informed personalization, as most frameworks focus on physical variables but neglect psychological factors (e.g., risk tolerance, urgency), limiting their ability to capture individual differences in lane-change motivations. 2) Limited holistic understanding of traffic context, most frameworks lack consideration of high-level and interpretable indicators (e.g., traffic pressure) in comprehensively assessing dynamic traffic scenarios, limiting their capacity for human-like contextual understanding. 3) Lack of transparent and interpretable decision logic, as many frameworks operate as black boxes with opaque reasoning processes, hindering human
Chen, Yanbo, Chen, Jiaqi, Yu, Huilong, Xi, Junqiang
Fuzzy Logic-Based Traction Control for a 4WD In-Wheel Electric Formula Vehicle2025-36-011412/18/2025
Vehicle dynamic control is crucial for ensuring safety, efficiency and high performance. In formula-type electric vehicles equipped with in-wheel motors (4WD), traction control combined with torque vectoring enhances stability and optimizes overall performance. Precise regulation of the torque applied to each wheel minimizes energy losses caused by excessive slipping or grip loss, improving both energy efficiency and component durability. Effective traction control is particularly essential in high-performance applications, where maintaining optimal tire grip is critical for achieving maximum acceleration, braking, and cornering capabilities. This study evaluates the benefits of Fuzzy Logic-based traction control and torque distribution for each motor. The traction control system continuously monitors wheel slip, ensuring they operate within the optimal slip range. Then, torque is distributed to each motor according to its angular speed, maximizing vehicle efficiency and performance
Oliveira, Vivian Fernandes, Hayashi, Daniela Tiemi, Dias, Gabriel Henrique Rodrigues, Andrade Estevos, Jaqueline, Guerreiro, Joel Filipe, Ribeiro, Rodrigo Eustaquio, Eckert, Jony Javorski
An Adaptive Speed Prediction Method for Tracked Vehicles2025-99-032512/17/2025
This paper addresses the issue of decreased speed prediction accuracy in tracked vehicles due to external noise during operation, and proposes an adaptive speed prediction method based on fuzzy logic. Traditional prediction methods based on physical models struggle to handle the complex dynamic characteristics unique to tracked vehicles, while GPS-based speed measurement methods have poor reliability in areas with signal obstructions. In this study, Hall sensors are used to collect real-time motor speed data, which is preprocessed through mean filtering and outlier removal, and a piecewise linear regression model is established. On this basis, the fitting parameters are dynamically adjusted using fuzzy logic. Experimental results show that during acceleration (0→1.2 m/s), deceleration (1.2→0 m/s), and constant speed (0.4/0.8/1.2 m/s) phases, the maximum absolute error of this method is less than 0.234 m/s (deviation less than 20%), and the standard deviation is all below 5% of the
Yang, Xinyu, Yu, Wenjie, Li, Ping, Zhang, Guoliang
Robust Lateral Trajectory Tracking of Intelligent Vehicles by Using Fuzzy Adaptive Dynamic Model Predictive Control10-10-02-000912/3/2025
To further improve the smoothness and robustness of lateral trajectory tracking for intelligent vehicles under complex operating conditions, this study proposes and experimentally validates a fuzzy adaptive dynamic model predictive control (FADMPC) strategy on the basis of model predictive control (MPC) framework. Thereinto, a three-degrees-of-freedom vehicle dynamics model serves as the predictive model, and a recursive least-squares algorithm with a forgetting factor is used to estimate tire cornering stiffness, thereby improving model fidelity. A whale optimization algorithm (WOA)–based adaptive horizon scheduler is devised to address the sensitivity of the prediction horizon to vehicle speed and road friction, and a fuzzy regulator adjusts the weight on the lateral displacement error in the objective function in real time. Hardware-in-the-loop tests on jointed and split-road surfaces show that compared with adaptive dynamic MPC, traditional MPC, and linear quadratic regulator, the
Teng, Fei, Jin, Liqiang, Wang, Junnian, Yang, Chen, Fan, Jiapeng, Qiu, Neng, Li, Andong, Zhou, Yanbo
Energy-Efficient Thermal Management in Electric Vehicles Using Fuzzy Logic Control2025-28-041510/30/2025
The rapid rise in electric vehicle (EV) adoption demands innovative thermal management solutions to boost battery performance and passenger comfort. This paper introduces a novel control strategy for simultaneous battery and cabin cooling in EVs, utilizing a two-stage fuzzy logic controller. The proposed system incorporates a detailed plant model to simulate real-world conditions and dynamically optimize compressor speed, ensuring energy-efficient thermal management. In the first stage, the fuzzy controller sets the initial compressor speed based on primary inputs such as battery and cabin temperatures. The second stage fine-tunes this speed by considering secondary parameters like condenser and chiller pressures, along with the power output ratio from the plant model. This multi-stage approach guarantees efficient cooling for both the battery and cabin while maintaining safe operating conditions. Our research showcases the efficacy of this control strategy in achieving optimal thermal
Ponangi, Babu Rao, Meduri, Sunil, Pudota, Praveen, J, Anandu
Multivariable Fuzzy Sliding-Mode Torque Control for Internal Combustion Engines03-18-05-00327/21/2025
Internal combustion engine torque control presents a persistent challenge due to pronounced nonlinearities, parametric uncertainties, and time-varying dynamics. While conventional controllers like the proportional–integral derivative (PID) are widely implemented, they often struggle to deliver high-performance results under transient conditions. To address this gap, this work introduces and experimentally validates a novel torque controller with fuzzy sliding-mode controller (FSMC) architecture, a hybrid control not previously applied to the domain of engine torque regulation. The proposed FSMC is specifically engineered to systematically mitigate the effects of system nonlinearities by integrating the robustness of sliding-mode theory with the adaptive, chattering-suppression capabilities of fuzzy logic. This study details the controller’s development, implementation, and rigorous experimental validation on an ethanol-fueled engine via a dynamometer test bench. The controller’s
Silva, Marcos Henrique Carvalho, Maggio, André Vinícius Oliveira, Laganá, Armando Antônio Maria, Pereira, Bruno Silva, Justo, João Francisco
A Control Strategy for Height Adjustment in Closed-Loop Air Spring Suspension System2025-01-87984/1/2025
This study examines a closed air spring suspension system. To address issues such as over-inflation, over-deflation, and excessive overshoot during vehicle height adjustment, a threshold control method is implemented. This method controls the triggering conditions for height adjustment and effectively reduces overshoot while enhancing precision. Experimental results indicate that this control strategy decreases overshoot and improves accuracy. However, risks are associated with varying threshold settings across different control modules, which can lead to over-control. A fuzzy PID controller is developed to resolve this issue. This controller adjusts PID parameters in real time based on fuzzy rules, thereby refining height adjustments. During testing, it was found that the degree of electromagnetic valve opening could not be controlled by the fuzzy PID controller. Therefore, a control strategy to adjust the compressor speed is designed. Experiments show that the fuzzy PID controller
Zheng, Guoqing, Yin, Zhihong, Chen, Shiwen, Shangguan, Wen-Bin
Fuzzy Sliding Mode Control of Intelligent Electric Vehicles Based on Phase Plane Stability Domain Boundaries2025-01-88024/1/2025
In order to effectively improve the chassis handling stability and driving safety of intelligent electric vehicles (IEVs), especially in combing nonlinear observer and chassis control for improving road handling. Simultaneously, uncertainty with system input, are always existing, e.g., variable control boundary, varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis prescribed performance control strategy of IEVs become a hot topic in both academia and industry. To issue the above mentioned, a fuzzy sliding mode control method based on phase plane stability domain is proposed to enhance the vehicle’s chassis performance during complex driving scenarios. Firstly, a two-degree-of-freedom vehicle dynamics model, accounting for tire non-linearity, was established. Secondly, combing with phase plane theory, the stability domain boundary of vehicle yaw rate and side-slip phase plane based
Liao, Yinsheng, Wang, Zhenfeng, Guo, Fenghuan, Deng, Weili, Zhang, Zhijie, Zhao, Binggen, Zhao, Gaoming
Smart Machining of Titanium Grade 7: ANFIS Application for Process Parameter Prediction2025-28-01602/7/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has grown increasingly intricate. This study specifically examines Titanium Grade 7 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modelling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 7 provides more flexibility, efficiency, and accuracy in
Natarajan, Manikandan, Pasupuleti, Thejasree, D, Palanisamy, Kiruthika, Jothi, Silambarasan, R
Predictive Grey-ANFIS Model for High-Precision Machining of Nimonic Alloy in Automobiles2025-28-00432/7/2025
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Nimonic Alloy material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Nimonic Alloy is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's method and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that influences the intended performance criteria. In order to
Natarajan, Manikandan, Pasupuleti, Thejasree, C, Navya, Kiruthika, Jothi, Silambarasan, R
AI-Driven Optimization of Advanced Machining for Haste Alloy by ANFIS Method2025-28-01412/7/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study primarily examines the use of Haste alloy in vehicle applications and suggests creating regression models to predict performance parameters in ECM. The experiments are formulated based on Taguchi's ideas, and mathematical equations are derived using multiple regression models. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the statistical significance of process parameters that impact performance indicators. The proposed regression models for Haste alloy are more versatile
Natarajan, Manikandan, Pasupuleti, Thejasree, D, Palanisamy, Silambarasan, R, Krishnamachary, PC
Revolutionizing Titanium Alloy Machining: ANFIS Model for Advanced Machining Performance Optimization2025-28-00462/7/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in electrically conductive materials, regardless of their hardness. Due to the growing demand for superior products and the necessity for quick design adjustments, decision-making in the manufacturing industry has become increasingly complex. This study specifically examines Titanium Grade 19 and suggests the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the goal of concurrently maximizing material removal rate, minimizing surface roughness, and achieving precise geometric tolerances. Analysis of variance (ANOVA) is used to assess the relevance of process characteristics that impact these performance measures. The ANFIS model presented for Titanium Grade 19 provides more flexibility, efficiency, and accuracy in comparison to
Pasupuleti, Thejasree, Natarajan, Manikandan, Raju, Dhanasekar, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Enhanced Aero Engine Components Manufacturing through Predictive Models for Cupronickel Machining2025-28-01372/7/2025
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, regardless of their level of hardness. Due to the growing demand for superior products and the necessity for quick design changes, decision-making in the manufacturing industry has become increasingly intricate. The preliminary intention of this work is to concentrate on Cupronickel and suggest the creation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the purpose of predictive modeling in ECM. The study employs a Taguchi-grey relational analysis (GRA) methodology to attain multi-objective optimization, with the target of maximizing material removal rate, minimizing surface roughness, and simultaneously achieving precise geometric tolerances. The ANFIS model suggested for Cupronickel provides more flexibility, efficiency, and accuracy compared to conventional approaches, allowing for enhanced monitoring and control in ECM operations
Pasupuleti, Thejasree, Natarajan, Manikandan, Ramesh Naik, Mude, Kiruthika, Jothi, Silambarasan, R
Optimization of Wire Electrical Discharge Machining Parameters for Invar 36 Material Using Regression Modeling2025-28-01222/7/2025
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
Pasupuleti, Thejasree, Natarajan, Manikandan, Raju, Dhanasekar, Krishnamachary, PC, Silambarasan, R
Development of ANFIS Predictive Model for Additive Manufacturing (Fusion Deposition Modeling) of PETG Material for Automotive Components2025-28-01242/7/2025
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has emerged as a revolutionary method for fabricating complex geometries using a variety of materials. Polyethylene terephthalate glycol (PETG) is a thermoplastic material that is biodegradable and environmentally friendly, making it a preferred choice in additive manufacturing (AM) due to its affordability and ease of use. This study aims to optimize the FDM settings for PETG material and investigate the impact of key process parameters on printing performance. An experimental study was conducted to evaluate the influence of crucial factors in FDM, including layer thickness, infill density, printing speed, and nozzle temperature, on significant outcomes such as dimensional accuracy, surface quality, and mechanical properties. The use of the Grey Relational Analysis (GRA) approach enabled a systematic assessment of multi-performance characteristics, facilitating the optimization of the FDM process. The findings
Pasupuleti, Thejasree, Natarajan, Manikandan, Kumar, V, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Advanced AI Tools for Haste Alloy Machining: ANFIS Approach to Advanced Machining Optimization2025-28-01562/7/2025
The intention of this exploration is to evolve an optimization method for the Electrochemical Machining (ECM) process on Haste alloy material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Haste alloy is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
Pasupuleti, Thejasree, Natarajan, Manikandan, Ramesh Naik, Mude, Somsole, Lakshmi Narayana, Silambarasan, R
Development of AI Prediction Tool for Fused Deposition Modeling of ABS Material for Automobile Applications2025-28-01272/7/2025
Fused Deposition Modeling (FDM), a form of Additive Manufacturing (AM), has emerged as a groundbreaking technology for the production of complex shapes from a variety of materials. Acrylonitrile Butadiene Styrene (ABS) is an opaque thermoplastic that is frequently employed in additive manufacturing (AM) due to its affordability and user-friendliness. The purpose of this investigation is to enhance the FDM parameters for ABS material and develop predictive models that anticipate printing performance by employing the Adaptive Neuro-Fuzzy Inference System (ANFIS). Through experimental trials, an investigation was conducted to evaluate the influence of critical FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including mechanical properties, surface polish, and dimensional accuracy. The utilization of design of experiments (DOE) methodology facilitated a systematic examination of parameters. A predictive model was
Natarajan, Manikandan, Pasupuleti, Thejasree, Kumar, V, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Intelligent Vehicle Trajectory Tracking Control Based on Variable Universe Fuzzy Rule Speed Planning and Piecewise Preview Model Prediction02-18-01-00062/6/2025
Intelligent vehicles can utilize a variety of sensors, computing, and control technologies to autonomously perceive the environment and make decisions to achieve safe, efficient, and automated driving. If the speed planning of intelligent vehicles ignores the vehicle dynamics state, it leads to unreasonable planning speed and is not conducive to improving the accuracy of trajectory tracking control. Meanwhile, trajectory tracking usually does not consider the road and speed information beyond the prediction horizon, resulting in poor tracking precision that is not conducive to improving driving comfort. To solve these problems, this study proposes a new longitudinal speed planning method based on variable universe fuzzy rules and designs the piecewise preview model predictive control (PPMPC) to realize the vehicle trajectory tracking. First, the three-degrees-of-freedom vehicle dynamics model and trajectory tracking model are established and verified. Then, the variable universe fuzzy
Zhang, Jie, Teng, Shipeng, Gao, Jianjie, Zhou, Xingxing, Zhou, Junchao
Vibration Suppression of a Flexible Appendage and Pitch Angle Fuzzy Regulation of a Spacecraft01-18-01-00042/5/2025
In this work, the large-angle rotational movement and vibration suppression of a flexible spacecraft are carried out based on an adjustable system. First the spacecraft model is transformed into a canonical affine control form, then two fuzzy systems are used: The first (of Takagi–Sugeno type) estimates the feedback linearization control law as a whole, while the second (of Mamdani type) adjusts and stabilizes the control parameters using the gradient descent technique and based on the minimization of the control error rather than the tracking error. Stability results are presented in terms of Lyapunov’s theory, and simulation tests illustrate the significant transient robustness of the closed-loop system against perturbations, the accurate trajectory control, and vibration suppression of the flexible spacecraft. Consequently, as will be shown later, the error will stay confined and converges quickly to zero, confirming the smoothing property of the proposed method using fuzzy logic
Bahita, Mohamed
A Two-Layer Fuzzy Control Energy Management Method for Hydrogen Fuel Cell Heavy-Duty Trucks Considering Fuel Cell Health State2025-01-70921/31/2025
Hydrogen fuel cell trucks have enormous development potential in the pursuit of global carbon neutrality and sustainable development. However, their commercialization and mass production are facing challenges in various aspects, especially the durability problem of fuel cells. This paper is intended to set up a high-power hydrogen fuel cell system (FCS) model, considering the fuel cell degradation factors, and based on this, proposes a two-layer fuzzy energy management strategy (EMS) to optimize the life of fuel cell and the total energy consumption of the vehicle. The first control layer provides real-time energy distribution efficiently from multiple sources and thus allows flexibility in energy supply. The second layer regulates the dynamic adjustment of fuel cell output power with degradation of both fuel cells and batteries considered, to make the prolonging of system lifetime possible. In this respect, the equivalent hydrogen consumption, which incorporates fuel cell degradation
Hou, Quan, Wang, Han, Zhu, Dan
Enhanced Adaptive Cruise Control Strategies Considering Front and Rear Vehicles10-09-01-00071/29/2025
Adaptive cruise control (ACC) systems have increasingly become more robust in adapting to the motion of the preceding vehicle and providing safety and comfort to the driver. But conventional ACC hangs with a concern for rear-end safety in the presence of traffic or aggressive car maneuvers. It often leads to getting dangerously close to the vehicle behind in scenarios where there is less space and time for the rear vehicle to adjust. This research article develops an ACC approach that considers the rear vehicle in addition to the front vehicle, thereby ensuring safety with the rear vehicle without compromising the safety of the front vehicle. Two novel methodologies are devised to enhance the ACC system. The first approach involves utilizing fuzzy logic to associate the inputs with the throttle and brake based on the inference rules within a fuzzy logic controller overseeing both vehicles. The other utilizes a cascaded model predictive control (MPC) system framework that integrates a
Sharma, Vishrut, Sengupta, Somnath, Ghosh, Susenjit
Multi-Objective Welding Optimization for AA5052 Using Taguchi-Fuzzy Approach05-18-03-00181/24/2025
This study investigates the influence of tungsten inert gas (TIG) welding parameters on the dilution and hardness of AA5052 aluminum alloy. Employing Taguchi’s L27 orthogonal array, the research systematically explores the effects of current, voltage, and welding speed. Analysis of the experimental data utilizes signal-to-noise ratio, analysis of variance (ANOVA), and regression techniques. The study compares a traditional regression model with a fuzzy logic approach for result validation, finding that the latter exhibits marginally better predictive accuracy. Optimal welding parameters are identified as 150 A current, 20 V voltage, and 45 mm/s welding speed, yielding a maximum dilution of 52.81% and hardness of 145.3 HV 0.5. Current emerges as the most significant factor influencing both dilution and hardness. Microstructural examination, hardness profiling, and tensile testing of specimens welded under optimized conditions reveal a characteristic hardness distribution across the weld
Omprakasam, S., Raghu, R., Balaji Ayyanar, C.
PDCA-Guided Development of Fuzzy Logic Controller for Autonomous Vehicle Cruise Control and Collision Avoidance2024-36-018812/20/2024
The advancement of the automotive industry towards automation has fostered a growing integration between this field and automation. Future projects aim for the complete automation of the act of driving, enabling the vehicle to operate independently after the driver inputs the desired destination. In this context, the use of simulation systems becomes essential for the development and testing of control systems. This work proposes the control of an autonomous vehicle through fuzzy logic. Fuzzy logic allows for the development of sophisticated control systems in simple, easily maintainable, and low-cost controllers, proving particularly useful when the mathematical model is subject to uncertainties. To achieve this goal, the PDCA method was adopted to guide the stages of defining the problem, implementation, and evaluation of the proposed model. The code implementation was done in Python and validated using different looping scenarios. Three linguistic variables were used, one with three
Branco, César Tadeu Nasser Medeiros, Santos, Rafael Celestino
A Driving Behavior Decision Method Based on Gradient Boosting Tree Algorithm2024-01-703012/13/2024
In order to reduce the incidence of traffic accidents and improve passengers’ driving experience, intelligent driving technology has attracted more and more attention. The core content of intelligent driving technology includes environment perception, behavior decision-making and control follow-up. Simulating driver’s behavior decision-making based on multi-source heterogeneous environment information is the key to liberate drivers and become the focus and difficulty of intelligent driving technology. Aiming at this key problem, this paper presents a design method of driving behavior decision maker based on machine learning after fuzzy classification of historical data. Firstly, 1000 sets of driving environment-decision results database are generated randomly according to driving rules and driving state. A fuzzy classification rule is established to classify driving environment information such as speed and relative distance. Then, a driving behavior decision maker is designed based on
Li, Hongluo, Xia, Hongyang, Huang, Yongxian, Xu, You, Xu, Wei
Development of ANFIS Predictive Model for Wire Electrical Discharge Machining of Cupronickel Material2024-28-023912/5/2024
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Pasupuleti, Thejasree, Natarajan, Manikandan, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R.
Optimization and ANFIS Predictive Modeling of Additive Manufacturing (Fused Deposition Modeling) for PLA Material2024-28-024012/5/2024
Additive Manufacturing (AM), specifically Fused Deposition Modeling (FDM), has become a revolutionary technology for creating intricate shapes using different materials. Polylactic Acid (PLA) is a biodegradable thermoplastic that is commonly used in additive manufacturing (AM) because of its environmentally friendly properties, affordability, and ease of use. The objective of this study is to optimize the FDM parameters for PLA material and create predictive models using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast printing performance. An investigation was carried out through experimental trials to examine the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes such as dimensional accuracy, surface finish, and mechanical properties. The utilization of design of experiments (DOE) methodology enabled a methodical exploration of parameters. A predictive model using ANFIS was created to
Pasupuleti, Thejasree, Natarajan, Manikandan, Kiruthika, Jothi, Ramesh Naik, Mude, Silambarasan, R
Optimization and ANFIS Predictive Modeling of Wire Electrical Discharge Machining for Invar 36 Material2024-28-024312/5/2024
Wire Electrical Discharge Machining (WEDM) is a highly accurate machining approach that is well-known for its capability to create intricate forms in materials with high levels of hardness and intricate geometries. Invar 36, a nickel-iron alloy, is extensively utilized in industries that demand exceptional dimensional stability across a wide temperature range. The objective of this exploration is for optimizing the WEDM parameters of Invar 36 material. Additionally, a predictive model called Adaptive Neuro-Fuzzy Inference System (ANFIS) will be developed to forecast the machining performance. The study involved conducting experimental trials to analyze the influence of crucial factors in WEDM. These parameters included pulse-on time (Ton), pulse-off time (Toff), and current. The objective was to examine their influence on key performance indicators such as material removal rate (MRR), surface roughness (Ra). The methodology of Design of Experiments (DOE) enabled a systematic
Natarajan, Manikandan, Pasupuleti, Thejasree, Kiruthika, Jothi, Katta, Lakshmi Narasimhamu, Silambarasan, R
Development of ANFIS Predictive Model for Additive Manufacturing of TPU Material2024-28-002510/17/2024
Additive Manufacturing (AM) techniques, particularly Fusion Deposition Modeling (FDM), have received considerable interest due to their capacity to create complex structures using a diverse array of materials. The objective of this study is to improve the process control and efficiency of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material by creating a predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS). The study investigates the impact of FDM process parameters, including layer height, nozzle temperature, and printing speed, on key printing attributes such as tensile strength, flexibility, and surface quality. Several experimental trials are performed to gather data on these parameters and their corresponding printing attributes. The ANFIS predictive model is built using the collected dataset to forecast printing characteristics by analyzing input process parameters. The ANFIS model utilizes the learning capabilities of neural networks
Pasupuleti, Thejasree, Natarajan, Manikandan, D, Palanisamy, A, Gnanarathinam, Umapathi, D, Kiruthika, Jothi
Method for Modeling Automotive Manufacturing Equipment Preventive Maintenance Derived by Multi-Criteria Decision and Synthesized Weibull Distribution2024-01-509610/8/2024
To avoid equipment failures in automotive manufacturing activities, particular attention is paid to the design of an effective preventive maintenance strategy model for automotive component processing equipment. The selection of appropriate maintenance intervals as well as the equilibrium between the benefits and costs should be the primary challenges in high-quality maintenance process. In this study, a reliable preventive maintenance strategy model is proposed and the aim is to suggest an appropriated approach for the selection of maintenance intervals from a comprehensive view of importance, hazard, and maintenance cost. First and foremost, a new Fermatean fuzzy entropy (FFE) measure method on the basis of analytic hierarchy process (AHP) is innovatively employed to access more objective weights of each indicator. Moreover, a more objective scoring of importance and hazard indicator is executed to aggregate the expert group judgments. Furthermore, this study emphasizes the
Ma, Zexin, Pan, Zhesheng, Wang, Chengxiang, Wei, Mingxin, Yu, Wenbin, Li, Guoxiang, Zhao, Feiyang, Zhu, Sipeng
A Hardware-in-the-Loop Platform for Developing a Fuel Cell Hybrid Electric Microcar: Fuel Cell Stack Sizing Using a Real-Time Testing Approach2024-24-00069/18/2024
Sustainable mobility is a pressing challenge for modern society. Electrification of transportation is a key step towards decarbonization, and hydrogen Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer a promising alternative to Battery Electric Vehicles (BEVs), especially for long-range applications: they combine a battery system with a fuel cell, which provides onboard electric power through the conversion of hydrogen. Paramount importance is then given to the design and sizing of the hybrid powertrain for achieving a compromise between high performance, efficiency, and low cost. This work presents a Hardware-in-the-Loop (HIL) platform developed for designing and testing the powertrain layout of an FCHEV. The platform comprises two systems: a simulation model reproducing the dynamics of a microcar and a hardware system for the fuel cell hybrid electric powertrain. The former simulates the vehicle's behavior, while the latter is composed of a 2kW real fuel cell stack and a 100Ah Li-ion
Bartolucci, Lorenzo, Cennamo, Edoardo, Cordiner, Stefano, Donnini, Marco, Grattarola, Federico, Mulone, Vincenzo
Personalized Design of Variable Transmission Ratio and Selection Switching Strategies Considering Drivers’ Steering Characteristics10-08-04-00289/14/2024
In order to meet the driving characteristics and needs of different types of drivers and to improve driving comfort and safety, this article designs personalized variable transmission ratio schemes based on the classification results of drivers’ steering characteristics and proposes a switching strategy for selecting variable transmission ratio schemes in response to changes in driver types. First, data collected from driving simulator experiments are used to classify drivers into three categories using the fuzzy C-means clustering algorithm, and the steering characteristics of each category are analyzed. Subsequently, based on the steering characteristics of each type of driver, suitable speed ranges, steering wheel travel, and yaw rate gain values are selected to design the variable transmission ratio, forming personalized variable transmission ratio schemes. Then, a switching strategy for variable transmission ratio schemes is designed, using a support vector machine to build a
Chen, Chen, Zheng, Hongyu, Zong, Changfu
Improved Monitoring and Classification of Engine Oil Condition through Two Machine Learning Techniques04-18-01-00059/14/2024
This study explores the effectiveness of two machine learning models, namely multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), in advancing maintenance management based on engine oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe, Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine conditions. Remarkably, both models exhibited high accuracy, achieving an average precision of 94%. While the radial basis function (RBF) model, as presented in a referenced article, surpassed ANFIS, this comparison underscored the transformative potential of artificial intelligence (AI) tools in the realm of maintenance management. Serving as a proof-of-concept for AI applications in maintenance management, this study encourages industry stakeholders to explore analogous methodologies. Highlights Two machine learning models, multilayer
Pourramezan, Mohammad-Reza, Rohani, Abbas
Modelling and Simulation of Electronic Brake Booster with Fuzzy Logic Control2024-01-30529/8/2024
On one hand, simulation tools are widely used to study and examine new technologies before building prototypes. It is a cost and time saver if it is mathematically modeled with and simulated in real time with sufficient fidelity. On the other hand, the expansion of electric and hybrid vehicle development requested advancing the Electronic Brake Booster (EBB) technologies. In this paper, a simulation tool for the EBB is developed to simulate the performance in real time with a very quick response compared to the previous models with a novel fuzzy logic control (FLC) for the position tracking control. The configuration of the EBB is established, and the system model, including the permanent magnet synchronous motor (PMSM), a double reduction transmission (gears and a ball screw), a servo body, a reaction disc, and the hydraulic load, is modeled. The load-dependent friction has been compensated by using the Karnopp-friction model. FLC has been used for the control algorithm. The control
Soliman, Amr M.E., Kaldas, Mina M., Soliman, Aref M.A., Huzayyin, Ahmed
Research on Network Security Situation Prediction Algorithm Combining Intuitionistic Fuzzy Sets and Deep Neural Networks12-07-03-00224/17/2024
The expansion of the internet has made everyone’s personal and professional lives more transparent. There are network security issues because people like sharing resources under the right conditions. Academics have demonstrated significant interest in situation awareness, which includes situation prediction, situation appraisal, and event detection, rather than focusing on the security of a single device in the network. Multi-stage attack forecasting and security situation awareness are two significant issues for network supervisors because the future usually is unknown. Hence, this study suggests combined intuitionistic fuzzy sets and deep neural network (CIFS-DNN) for network security situation prediction. The goal is to provide network administrators with a resource they can use as a point of reference while they formulate and carry out preventive actions in the event of a network assault. The job requires differentiating between the event of an assault and a typical instance, as
Gao, Hui, Guo, Liang
Fuzzy Control of Regenerative Braking on Pure Electric Garbage Truck Based on Particle Swarm Optimization2024-01-21454/9/2024
To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers. Achieve one-time distribution of the front axle braking force; the second layer, according to the magnitude of the braking intensity, divides the braking conditions into light braking, moderate braking and emergency braking, and realizes braking under the three
Zhang, Yu
Enhancing Lateral Stability in Adaptive Cruise Control: A Takagi-Sugeno Fuzzy Model-Based Strategy2024-01-19624/9/2024
Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system. And the input penalty factor of the adaptive cruise control system is designed as a
Yan, Yang, Xin, Yafei, Zheng, Hongyu
Vehicle Braking Performance Improvement via Electronic Brake Booster10-08-01-00052/10/2024
Throughout the automobile industry, the electronic brake boost technologies have been widely applied to support the expansion of the using range of the driver assist technologies. The electronic brake booster (EBB) supports to precisely operate the brakes as necessary via building up the brake pressure faster than the vacuum brake booster. Therefore, in this article a novel control strategy for the EBB based on fuzzy logic control (FLC) is developed and studied. The configuration of the EBB is established and the system model including the permanent magnet synchronous motor (PMSM), a two-stage reduction transmission (gears and a ball screw), a servo body, reaction disk, and the hydraulic load are modeled by MATLAB/Simulink. The load-dependent friction has been compensated by using Karnopp friction model. Due to the strong nonlinearity on the EBB components and the load-dependent friction, FLC has been used for the control algorithm. The control concept focused on transforming the
Soliman, Amr M.E., Kaldas, Mina M., Soliman, Aref M.A., Huzayyin, A.S.
Hydrodynamic Flow Characteristics Prediction for Bluff Body Wake via Novel Adaptive Neuro-Fuzzy Controller Avoiding Fuzzy Rule Explosion2023-01-508111/22/2023
This study analyses the effect of Reynolds number (Re) and bluff body shape (quantified by shape factor SF) variation on various hydrodynamic characteristics of unsteady bluff body flow, such as Strouhal number, maximum lift coefficient, and mean drag coefficient. The study initially examines a relationship among these characteristics and further utilizes artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) controllers for their precise prediction. The results from real-time computational fluid dynamics (CFD) experimentations were gathered and considered to train ANN controllers. A novel ANFIS controller has been designed using only three membership functions thus solving the problem of fuzzy rule explosion. The results indicate that both the ANN and ANFIS controllers can precisely predict these hydrodynamic flow characteristics as validated through minimal values of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage
Kharola, Ashwani, Dobriyal, Ritvik, Sharma, Rakesh Chandmal, Sharma, Neeraj, Sharma, Ashwini, Raturi, Anuj
Artificial Intelligence Model for Machinability Investigations on Drilling of AA6061 with Micro Textured Tool for Automobile Applications2023-28-008211/10/2023
Considering the advancements in manufacturing industries, which are crucial for economic growth, there is a substantial demand for exploration and analysis of advanced materials, especially alloy materials, to enable efficient utilization of new technologies. Lightweight and high-strength materials, like aluminium alloys, are highly recommended for various applications that necessitate both strength and resistance to corrosion, such as automobile, marine and high-temperature applications. Therefore, there is a significant need to investigate and analyse these materials to facilitate their effective application in manufacturing sectors. This study investigates the machinability of drilling AA6061 using a micro-textured tool and proposes an Adaptive Neuro Fuzzy Inference System (ANFIS) model for investigating the machinability of drilling AA6061 aluminium alloy with a micro-textured uncoated tool. The ANFIS model considers various input parameters such as spindle speed, feed rate, and
Katta, Lakshmi Narasimhamu, Natarajan, Manikandan, Pasupuleti, Thejasree, Siva Rami Reddy, Narapureddy, Sivaiah, Potta
Renewal Fuzzy Logic Energy Management System2023-01-168710/31/2023
This work proposes a unique control method consisting of ameliorated with reinforcement learning renewal module. The combined fuzzy logic and reinforcement learning regime is utilized to promote robust energy management control in complex working conditions. The coupled optimization proposition tackles unforeseen disturbance by two-pronged approach, with fuzzy logic analyzing backbone power contribution schemes while reinforcement learning takes responsibility for improving a higher efficiency strategy. The vehicle dynamic parameters and energy map are co-modeled through learning extrapolation function. Fuzzy rule undergoes efficient feedback revival via modulating factors driven from multi-objective RL reward computation. Meanwhile, reinforcement learning system leverages adaptive fuzzy representation that generalizes coordination potential vectors, effectively extends exploration quality compared to vanilla learning strategy. To this end, this work effectively considers traction
Ouyang, Qianyu
Prediction of the Remaining Useful Life of the Proton Exchange Membrane Fuel Cell with an Integrated Health Index2023-01-701310/30/2023
To improve the prediction accuracy of the remaining useful life (RUL) of the proton exchange membrane fuel cell (PEMFC), an integrated health index (IHI) including electrical and non-electrical parameters of PEMFC is established, and the RUL prediction is conducted based on the above index. Firstly, several operating conditions including the PEMFC degradation information are selected according to the information theory method. Moreover, the IHI is established by the sequential quadratic programming method. Secondly, RUL predictions based on the power and IHI are conducted by the adaptive neuro fuzzy inference system (ANFIS), respectively. Finally, different results comparisons including power and IHI differences, differences between experimental and training/predicting results, amounts of different differences in training and predicting phases, and RUL prediction results are presented in detail. The results show that the accuracy of the RUL prediction based on the IHI is higher than
Fan, Lei, Zhou, Su, Wen, Chaokai, Gao, Jianhua
Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort10-07-04-003210/3/2023
Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system. These results of the tests show that the optimal preview controller designed using the GN both ∑-Π-ANN and Π-∑-ANN can accurately capture the complex dynamics of the MR damper and improve their damping
Shehata Gad, Ahmed, Darakhshan Jabeen, Syeda, Galal Ata, Wael
Interval Lower Singleton Fuzzy Optimal Controller Design of Magnetorheological Seat Suspension Integrated with Semi-Active Vehicle Suspension System2023-01-50669/22/2023
In this paper, semi-active MR main suspension system based on system controller design to minimize pitch motion linked with MR-controlled seat suspension by considering driver’s biodynamics is investigated. According to a fixed footprint tire model, the transmitted tire force is determined. The linear-quadratic Gaussian (LQG) system controller is able to enhance ride comfort by adjusting damping forces based on an evaluation of body vibration from the dynamic responses. The controlled damping forces are tracked by the signum function controllers to evaluate the supply voltages for the front and rear MR dampers. Based on the sprung mass acceleration level and its derivative as the inputs, the optimal type-2 (T-2) fuzzy seat system controller is designed to regulate the controlled seat MR damper force. The best rate for each linguistic variable is acquired by modifying the range between upper and lower membership functions (MFs), which enables accurate tracking of the seat-damping force
Shehata Gad, Ahmed
Design and Implementation of Adaptive and Artificial Intelligence Controller for Brushless Motor Drive Electric Vehicle14-13-01-00036/29/2023
Brushless direct current (BLDC) motor aims to obtain high efficiency when compared to conventional DC motors due to several reasons. But when it comes to the control then its control is much more complicated due to the requirement of a phase supply switching circuit. Usually, the conventional and classical proportional integral derivative (PID) controller is used but it is quite cumbersome to tune its fixed gains. APID controller is used where PID fails to fulfill the objectives in varying situations. So, the adaptive proportional integral derivative (APID) controller is utilized to enhance the results. An artificial neural network (ANN) controller is one of the recent control methods, which gives accurate and precise results and utilizes ANN to give more accurate results. But it lacks fuzzy logic, that is, human tendency, and finally, the artificial neuro-fuzzy inference system (ANFIS) controller is concluded as the best controller to limit the speed of the BLDC motor. ANFIS includes
Saxena, Aditi, Gupta, Amit, Tiwari, Nitesh
A Simple Prototype to Forecast High Ice Water Content Using TAT Anomalies as Training Data2023-01-14956/15/2023
In the last decades there have been many temporary engine failures, engine-related events and erroneous airspeed indication measurements that occurred by a phenomenon known as Ice Crystal Icing (ICI). This type of icing mainly occurs in high altitudes close to tropical convection in areas with a high concentration of ice crystals. Direct measurements or in-situ pilot observations of ICI that could be used as a warning to other air-traffic are rare to nearly non-existent. To detect those dangerous high Ice Water Content (IWC) areas with already existing airborne measurement instruments, Lufthansa analyzed observed Total Air Temperature (TAT) anomalies and used a self-developed search algorithm, depicting those TAT anomalies that are related to ice crystal icing events. To optimize the flight route for dispatchers several hours before the flight, e.g. for long distance flights through the intertropical convergence zone (ITCZ), reliable forecasts to identify hazardous high IWC regions are
Kalinka, Frank, Butter, Max, Jurkat, Tina, De La Torre Castro, Elena, Voigt, Christiane
Automotive Steering System and Its Controller Design for Intelligent Vehicles13-04-02-00114/24/2023
With the rapid development of intelligent vehicles technology, it is extremely urgent to solve environmental pollution and energy crisis. The electric intelligent vehicles technology can accelerate the world to move towards low carbonization and intelligence. In this article, one automatic steering system and its controller are designed with this electric vehicle as the verification platform. First, based on the digital mock-up (DMU) module of the CATIA digital prototype, the motion simulation of the automatic steering system is carried out. Then, the transient dynamics and fatigue analysis module from ANSYS Workbench 16.0 software is used to simulate and analyze the transmission mechanism. After verifying the reasonable strength of the real vehicle parts, the original platform steering system is reformed. Our intelligent vehicle uses a monocular charge-coupled device (CCD) to detect road marking lines and then employs a linear two degrees of freedom (2-DOF) vehicle model to establish
Zhao, Yibing, Chen, Yuqiao, Lv, Yanqing, Guo, Lie
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