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Adaptive Fixed-Time path tracking control for Autonomous Vehicles Subject to Full State Constraints and Unknown Tire Cornering Stiffness

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 8848-8863页

作者： Zhang, Tengfei Wang, Heng Zhao, Hui Che, Weiwei Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300191 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

This paper investigates the problem of path tracking for autonomous vehicles subject to unknown tire cornering stiffness and full state constraints. First, by analyzing the effects of nonlinear tire dynamics on path tracking performance, the tire cornering stiffnesses are treated as unknown parameters in the modeling process, which contributes to improving the robustness of the vehicle control system. Second, to handle the unknown tire cornering stiffness in system matrices and control coefficients, an adaptive fixed-time path tracking control strategy with high-gain observer-based output feedback is developed. Different from the existing methods, the lower bounds of unknown control coefficients are incorporated into the Lyapunov function to effectively eliminate the requirement of tire cornering stiffness and their bounds information. Moreover, the full state constraints, formulated based on the vehicle stability margins, are taken into account to enhance the dynamic stability of the vehicle. Based on the barrier Lyapunov stability theory, it is proven that the designed adaptive backstepping controller guarantees the fixed-time stability of the closed-loop system, and that all vehicle states remain within the constraints of stability region. Finally, the effectiveness of the proposed control scheme is demonstrated through simulation and comparison results.

关键词： Tires Vehicle dynamics control design Autonomous vehicles Convergence Adaptive systems Wheels Stability criteria control systems Uncertainty path tracking control unknown tire cornering stiffness adaptive backstepping control autonomous vehicles

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Adaptive path tracking control for autonomous vehicles with sideslip effects and unknown input delays

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2025年第3期47卷 444-453页

作者： Zhou, Xin Wang, Heng Li, Qing Wu, Libing Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ 30 Xueyuan Rd Beijing 100083 Peoples R China Univ Sci & Technol Liaoning Sch Sci Anshan Peoples R China

This paper presents an adaptive path-tracking control of autonomous ground vehicles with sideslip effects and time-varying input delays. To handle the problem of unknown time-varying input delays, the vehicle kinematic model is transformed into a novel chain model, and a delay-based auxiliary integral term is introduced for the estimation of the unknown terms caused by time-varying delays. To avoid modeling errors caused by existing linear approximation methods, a new adaptive path-tracking control strategy is proposed, such that the optimal tracking performance is achieved by appropriately adjusting certain design parameters. In addition, the inherent complexity explosion issue is avoided with the aid of a boundary estimation strategy. All signals of the closed-loop systems are guaranteed to be bounded. Simulation results illustrate the effectiveness of the method proposed.

关键词： Autonomous ground vehicles path tracking control time-varying input delays adaptive control complexity explosion

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Hybrid path tracking control for Autonomous Trucks: Integrating Pure Pursuit and Deep Reinforcement Learning With Adaptive Look-Ahead Mechanism

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 7098-7112页

作者： Han, Zhixuan Chen, Peng Zhou, Bin Yu, Guizhen Beihang Univ Minist Ind & Informat Technol Sch Transportat Sci & Engn Key Lab Autonomous Transportat Technol Special Veh Beijing 100191 Peoples R China

path tracking control is essential for ensuring the safe and efficient operation of autonomous trucks, but traditional methods often struggle with nonlinear vehicle dynamics. While deep reinforcement learning (DRL) approaches are model-free, they may lack the stability and interpretability required for reliable deployment. This study presents a hybrid control framework that combines Pure Pursuit (PP) with Proximal Policy Optimization (PPO) to enhance tracking accuracy and robustness. PP provides baseline stability and interpretability, while PPO refines control actions by optimizing policy gradients, ensuring better adaptability to nonlinear dynamics and complex driving conditions. An adaptive look-ahead mechanism, responsive to speed and curvature, dynamically adjusts preview distances using PPO-generated coefficients, facilitating early corrections during high-speed turns and enabling greater precision on sharp curves. A fusion training method, leveraging high-reward initialization and a decreasing learning rate, supports efficient exploration and stable convergence. The approach was validated in a high-fidelity simulation environment using PreScan, Simulink, and ROS, along with real-world experiments on a proportionally scaled intelligent vehicle chassis, demonstrating notable improvements in path tracking accuracy and robustness across varied path profiles.

关键词： Vehicle dynamics Heuristic algorithms Adaptation models Stability analysis Accuracy Optimization Deep reinforcement learning Autonomous vehicles Training Target tracking path tracking control deep reinforcement learning (DRL) adaptive look-ahead mechanism high-speed turns

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Active fault-tolerant path tracking control for autonomous vehicles considering actuator fault and model mismatch

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control ENGINEERING PRACTICE 2025年 162卷

作者： Chang, Yuan Hu, Manjiang Yang, Zeyu Bian, Yougang Lei, Zhiyong Qin, Hongmao Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Technol Vehicle Changsha 410082 Peoples R China Hunan Univ Wuxi Intelligent Control Res Inst Wuxi 214115 Peoples R China

The steering systems of autonomous vehicles may exhibit inherent deviations, resulting in large steady-state path tracking errors. The offline calibrations of the steering systems for a fleet of vehicles are time-consuming. Additionally, the model mismatches during cornering maneuvers compromise the control performance of model-based path tracking algorithms. To address these issues, an active fault-tolerant controller is proposed in this paper, incorporating the framework of nonlinear moving horizon estimation (NMHE) and nonlinear model predictive control (NMPC). The NMHE is employed to estimate the lumped uncertainties stemming from actuator faults and model mismatches, while the NMPC is used for accurate and smooth tracking of the reference path. The proposed controller is capable of online compensation for the steering deviations with less conservatism, significantly improving the path tracking accuracy, and enhancing the capabilities of handling constraints. Finally, the effectiveness of the controller in fault-tolerance and model mismatch correction is validated through simulation tests and real vehicle experiments.

关键词： path tracking control NMHE NMPC Actuator fault Model mismatch Autonomous vehicles

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The Simulation and path tracking control Study of Magnetic Miniature Soft Robots

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JOURNAL OF FIELD ROBOTICS 2025年第3期42卷 903-915页

作者： Wang, Sanxiu Dai, Zhenhao Lu, Qun Su, Chun-Yi Taizhou Univ Sch Aeronaut Engn Taizhou Zhejiang Peoples R China Zhejiang Dafeng Ind Co Ltd Hangzhou Zhejiang Peoples R China Concordia Univ Gina Cody Sch Engn & Comp Sci Montreal PQ Canada

Magnetic miniature soft robots hold significant potential in biomedical research, especially for targeted therapy, drug delivery, and cell manipulation. Precise path tracking control is crucial for these robots in complex biomedical applications. Here, we propose a Stanley path tracking control algorithm based on visual feedback for magnetic soft robots. First, a magnetic miniature soft crawling robot was designed and fabricated, and its crawling mechanism was detailed. Next, a simulation framework using the material point method (MPM) was constructed to simulate the movement and deformation of the miniature robot and to verify the proposed crawling mechanism. Finally, visual feedback technology was used to obtain the robot's position and posture, and the Stanley algorithm was applied for path tracking control in crawling mode. The effectiveness of the proposed path tracking control strategy has been verified through multiple experiments. Compared with the traditional Pure Pursuit control method, it has higher robustness and better control accuracy.

关键词： magnetic soft miniature robots material point method path tracking control visual servo

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Robust Optimal path tracking control for Intelligent Vehicles Equipped with Wheel Modules Based on Continuous Integral Sliding Mode control

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AUTOMOTIVE INNOVATION 2025年 1-20页

作者： Dong, Peng Li, Junqing Guo, Wei Zhang, Hanning Zhao, Peishen Xu, Xiangyang Wang, Shuhan Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beihang Univ Ningbo Inst Technol Ningbo 315832 Peoples R China

path tracking performed by multiple chassis actuators remains a significant yet challenging issue in intelligent vehicles. This paper presents a robust optimal path tracking control for intelligent vehicles equipped with a novel wheel module. A wheel module, named corner drive system (CDS), is proposed by integrating drive, brake, steering, and suspension subsystems. In light of the multi-actuator-integrate characteristic of the CDS, a reconfigurable concept is adopted to carry out the vehicle dynamics modeling. To realize an invariance system for both parametric and nonparametric uncertainties, an integral sliding mode control (ISMC) scheme is devised for desired path tracking by combining the advantages of linear quadratic regulator (LQR) in optimization and SMC in robustness. The chattering problem of the ISMC is analyzed and two continuous controllers for chattering elimination are proposed. A control allocation strategy is proposed to dynamically assign the virtual inputs generated by ISMC among four wheels to optimize vehicle stability and minimize energy dissipation. The superiority of the proposed control scheme is demonstrated under different chassis layouts, road friction conditions, and benchmark controllers on a CarSim-based high-fidelity vehicle model with simulation comparison. Subsequently, the Hardware-in-the-Loop (HiL) test is carried out to further evaluate the feasibility and real-time control performance. The quantitative results demonstrate the path tracking performance of the proposed robust optimal controller under both parametric and nonparametric uncertainties.

关键词： Intelligent vehicles path tracking control Robust optimal control Integral sliding mode control

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Slip-Compensation-Based path tracking control for Tracked Robots Using VBEKF and Backstepping control

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JOURNAL OF FIELD ROBOTICS 2025年

作者： Zhao, Xin Chen, Yuming Lu, En Tao, Wu Wang, Hui Zhang, Qian Fu, Rongbing Jiangsu Univ Sch Agr Engn Zhenjiang Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing Peoples R China Weichai Lovol Intelligent Agr Technol Co Ltd Weifang Peoples R China

Tracked robots are widely used in agriculture, military, mining, and other fields. During the traveling process of tracked robots, the complex interaction between the tracks and the ground causes slip, which leads to problems such as low path tracking accuracy and poor control stability. To solve this thorny problem, quantitative control compensation parameters are generally obtained through experiments, or extended Kalman filter (EKF) is designed based on Gaussian noise to estimate slip parameters during motion. However, the sensor measurement noise of tracked robots usually exhibits a non-Gaussian distribution in uneven terrain and variable soil conditions. Under such circumstances, these methods exhibit larger errors and demonstrate inadequate adaptability. Therefore, this paper proposes a variational Bayesian EKF (VBEKF) algorithm for slip parameters estimation, and designs a slip compensation path tracking controller to improve the accuracy and adaptability of the control system of tracked robots under complex operating conditions. The main contributions of this paper are as follows: (1) The non-Gaussian noise was re-modeled using the Student's t-distribution, and combined with variational Bayesian, the VBEKF algorithm was designed. This algorithm can more accurately estimate the slip parameters between the tracks and soil under complex and varying operating conditions, demonstrating enhanced adaptability. (2) Based on the backstepping control principle, a path tracking controller with slip parameter compensation was designed for tracked robots. This controller dynamically adjusts its output control based on the estimated slip parameters to eliminate the impact of slip between the tracks and soil on path tracking accuracy. Finally, the effectiveness of the method was demonstrated through simulations and experiments. This study can improve the adaptability and stability of tracked robots under complex and variable operating conditions, ensuring accurate and rapi

关键词： extended Kalman filter noise measurement path tracking control slip parameter estimation tracked robot variational Bayes

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Intelligent vehicle path tracking control with the accelerated bp neural network and dynamic model prediction

Intelligent vehicle path tracking control with the accelerat...

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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Liu, Jinwei Wang, Xudong Li, Licheng Chongqing College of Architecture and Technology Chongqing 401331 China School of Mechanical Engineering Chongqing Technology and Business University Chongqing 400067 China College of Electrical Engineering and Intelligent Manufacturing Chongqing Metropolitan College of Science and Technology Chongqing 402167 China

ISBN: (纸本)9781510690585

The intelligent vehicles are always driven in some uncertain and complex road environments, and facing the risk of dramatic changes in working conditions. In response to the impact of changes in vehicle state parameters, road curvature, and road friction coefficient on intelligent vehicle tracking control models, an adaptive model predictive controller based on acceleration neural networks is presented to improve control accuracy and effectiveness. According to the three degree of freedom dynamic model of the vehicle, the acceleration back propagation neural network (ABPNN) is used to adjust the tire turning stiffness and road friction coefficient in real-time online, and the tire turning stiffness and road friction coefficient are optimized for the vehicle dynamic model and road constraint conditions. A co-simulation platform is proposed with Simulink and Carsim software to track and simulate the path of vehicles under different speeds and scenarios. The results in this paper show that the adaptive controller has good tracking accuracy and stability under medium and low speed conditions. © 2025 SPIE.

关键词： Intelligent vehicle Model prediction path tracking control Simulation analysis

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path tracking control of Skid-steered Mobile Robot on the Slope Based on Fuzzy System and Model Predictive control

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2022年第4期20卷 1365-1376页

作者： Yue, Xiao Chen, Jiankui Li, Yiqun Zou, Rong Sun, Zhihao Cao, Xiaochuan Zhang, Song Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

Skid-steered mobile robots are often used in outdoor exploration due to their robust mechanical structure and high maneuverability. When they track reference path on a slope with boundaries, ensuring the tracking accuracy and stability of the skid-steered mobile robot is the major target. However, the gravity makes the relationship between wheels and ground more complex on the slope, and variational slope angle also makes it difficult for tracking control. The common control methods focus on plane motion, where only the plane forces are taken into account and the gravity is normally ignored. It may lead to some performance limitations such as the accuracy of motion on a slope. To address these problems, a model predictive control strategy combined with a fuzzy system is proposed in this paper, which has considered the dynamics of the body and wheels on the slope. We improved the two dimensional kinematics and dynamics model of the robot, which makes the three dimensional motion control more accurate. And the control method allows the robot to adapt to slopes with different angles and to make the path tracking stable to curvature mutation. Both experiment and simulation results demonstrate the effectiveness and superiority of the proposed model and method.

关键词： Fuzzy system model predictive control path tracking control wheeled mobile robot

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path tracking control for Underactuated Vehicles With Matched-Mismatched Uncertainties: An Uncertainty Decomposition Based Constraint-Following Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 12894-12907页

作者： Yang, Zeyu Huang, Jin Yin, Hui Yang, Diange Zhong, Zhihua Hunan Univ Coll Mech & Vehicle Engn Changsha 410006 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Chinese Acad Engn Beijing 100088 Peoples R China

This paper presents a robust path tracking control method by utilizing the ideology of constraint-following approach for uncertain underactuated autonomous vehicles. The uncertainties are bounded with an unknown boundary. They do not all fall within the range space of the input matrix. Based on kinematic relations between the desired path and vehicle, the path tracking task is transformed into an equality constraint of the vehicle lateral dynamics states. The control goal is to make the underactuated vehicle follow the constraint, thus realizing the desired tracking performance. The constraint-following robust control (CFRC) is designed in two steps. First, a servo control design for the nominal system is devised without considering uncertainty and initial constraint deviation. Second, the uncertainty is meticulously decomposed into matched and mismatched portions based on the geometric structural characteristics of the constraint dynamics system. As a result, since the mismatched uncertainty is orthogonal to the constraint-following geometric space, it ``disappears'' in the stability analysis. The matched uncertainty is estimated by a self-adjusting leakage type adaptive law. On this basis, a robust control is designed based on the estimated matched uncertainty. Through Lyapunov minimax analysis, the proposed control method guarantees the approximate constraint-following performance. Finally, the TruckSim-Simulink co-simulations and real vehicle experiments are presented. The results show that the proposed control can robustly realize excellent tracking performance in the presence of time-vary uncertainties.

关键词： Uncertainty Vehicle dynamics Tires Wheels tracking Task analysis Robust control path tracking control autonomous vehicles underactuated systems uncertainty decomposition robust control

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