Nonlinear robust wheel slip rate tracking control for autonomous vehicle with actuator dynamics

Zhang, Jiaxu; Zhou, Shiying; Zhao, Jian

doi:10.1177/1687814020925222

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…A simple yet effective vehicle dynamic model is the important basis of the integrated controller design for AFS and DYC. As shown in Figure 1, the absolute coordinate system Oxyz and the vehicle body coordinate system Bxyz with the vehicle centre of gravity (CG) for the origin are established, and the equations for vehicle dynamic model are derived by using D’Alembert principle (Xiong et al, 2018; Zhang et al, 2020).…”

Section: Vehicle Dynamic Modelmentioning

confidence: 99%

Integrated nonlinear robust adaptive control for active front steering and direct yaw moment control systems with uncertainty observer

Zhang

Zhou

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper presents an integrated nonlinear robust adaptive controller with uncertainty observer for active front wheel steering system and direct yaw moment control system. First, an integrated vehicle chassis control model is established as the nominal model with the additive and multiplicative uncertainties of the system. Secondly, an integrated nonlinear robust adaptive control law with the additive uncertainty observer is designed via Lyapunov stability theory to calculate the corrective yaw moment, and an adaptive law is designed based on projection correction method to online estimate and compensate the multiplicative uncertainty of the system. Then, the constrained optimal allocation problem of the corrective yaw moment is transformed into the nonlinear optimization problem, and the sequential quadratic programming method is used to solve the nonlinear optimization problem to coordinate active front wheel steering system and direct yaw moment control system. Finally, the performance of the proposed integrated nonlinear robust adaptive controller is verified via vehicle dynamics simulation software.

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Section: Vehicle Dynamic Modelmentioning

confidence: 99%

Integrated nonlinear robust adaptive control for active front steering and direct yaw moment control systems with uncertainty observer

Zhang

Zhou

et al. 2020

Transactions of the Institute of Measurement and Control

Self Cite

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“…The use of another type of sliding mode control can compensate for the limitations obtained by sliding first-order modes, in particular by using higher order sliding modes. Zhang et al [13], developed a quarter-vehicle aggregate uncertainty wheel slip ratio dynamics model used RBFNN with unknown optimal weight vectors adaptively adjusted to approximate and compensate for the aggregate uncertainty, and designed a new tracking differentiator to compute the derivatives of the desired wheel slip ratio, and the proposed control strategy is able to track the desired wheel slip ratio quickly and accurately. Hsu [14] proposed an intelligent exponential sliding mode control system based on a quarter-vehicle model, designed a function recursive fuzzy neural network uncertainty estimator to approximate the unknown nonlinear term of the ABS dynamics, and derived the parameter adaptive law in the sense of projection algorithms and Lyapunov's stability theorem, which ensured the stable control performance of ABS.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

Li,

2024

Mathematical Problems in Engineering

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Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC).

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“…For example, Hou et al 4 simplified the vehicle driving/braking system by the first-order transfer function and the properties of torque measured on the engine bench, then the accuracy of the simplified model was verified by comparing the simulation result with the experimental result. Jiaxu Zhang 5 presents a novel nonlinear robust wheel slip rate tracking control strategy for autonomous vehicle with actuator dynamics. Hsiu-Ming Wu 6 proposes a multiple sliding-mode control (MSMC) strategy based on the stator flux oriented vector scheme for speed control of three-phase AC induction motor (IM) drives in the presence of an external disturbance and uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

Zhang-qi

Jiang

Wei

et al. 2022

Advances in Mechanical Engineering

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Vehicle longitudinal dynamics system has the characteristics of being strongly non-linear, time-varying, and multiple-perturbed, so, it is difficult to build the mathematical model accurately. The control algorithms, based on accurate mathematical model, can hardly achieve the ideal effect, but control methods, which merely adopt input/output data (I/O) of a system, provides a solution. In this paper, by means of combing model-free adaptive control (MFAC) and sliding-mode control (SMC), the model-free adaptive sliding mode control (MFASMC) method is proposed. By comparison with feedback-feedforward control method, the MFASMC method can better improve the control effect and anti-disturbance performance. Meanwhile, the stability of MFASMC method was proven mathematically. Besides, the parameters of MFASMC method were optimized using genetic algorithm. Results of simulation and HiL test shows that the MFASMC method has fast response, strong robustness and smooth output. It would be better to apply it to the longitudinal dynamics control of intelligent vehicles.

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Nonlinear robust wheel slip rate tracking control for autonomous vehicle with actuator dynamics

Cited by 6 publications

References 32 publications

Integrated nonlinear robust adaptive control for active front steering and direct yaw moment control systems with uncertainty observer

Integrated nonlinear robust adaptive control for active front steering and direct yaw moment control systems with uncertainty observer

A Study on the Vehicle Antilock System Based on Adaptive Neural Network Sliding Mode Control

Model-free adaptive sliding mode control for intelligent vehicle longitudinal dynamics

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