Adaptive backstepping robust tracking control for stabilizing lateral dynamics of electric vehicles with uncertain parameters and external disturbances

Aiming to more accurately control the wheel speed of an electric vehicle (EV) driven by four in-wheel motors, a developed whale optimization algorithm-proportional–integral–derivative (KW-WOA-PID) control algorithm is proposed herein. In this study, mathematical and simulation models are built for EVs by analyzing the mechanical structures of EVs driven by four in-wheel motors. Simulations are conducted, and the driving and control requirements for the in-wheel motors are obtained. Then, mathematical and simulation models are built for a specific in-wheel motor. The whale optimization algorithm (WOA) is optimized by kent mapping and the adaptive weight coefficient to improve the ability of the algorithm to jump out of the local optimum and the convergence speed and convergence accuracy of WOA. Then the further simulations are conducted. The simulation results display that the maximum overshoot and adjustment time of the motor under KW-WOA-PID control are significantly optimized. Then, a speed-control bench test system is built for the in-wheel motor, and real-life experiments were conducted. The experimental results verify that KW-WOA-PID has higher control accuracy and a better response performance; accordingly, the developed control algorithm can meet driving requirements. The handling stability of EVs is effectively improved by controlling the motor speed.

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“…The EV driving equation (equation ( 8)) can be obtained by substituting equations ( 1)-( 6) into (7).…”

Section: Modeling and Simulation Of Ev Driven By Four In-wheel Motorsmentioning

confidence: 99%

“…5 Chhlonh et al 6 improved the handling stability of electric vehicles by proposing a hybrid Fuzzy-PI speed controller to control brushless DC motors. Pang et al 7 proposed a composite adaptive backstepping robust tracking controller to improve handling stability of EVs. Although the actual application is not yet mature.…”

Section: Introductionmentioning

confidence: 99%

In-wheel motor control system used by four-wheel drive electric vehicle based on whale optimization algorithm-proportional–integral–derivative control

Zhai

Ping

Zhang

et al. 2022

Advances in Mechanical Engineering

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“…However, the system parameters are exactly known in previous references. In reference [11], a composite adaptive backstepping robust tracking controller is presented, which aims to improve the lateral dynamics stability for an electric vehicle (EV) while considering the uncertain parameters and external disturbances. Te kinematic controller is proposed as an integrated backstepping controller for an autonomous tracked vehicle [12].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fault‐Tolerate Lateral Control for Autonomous Electric Vehicles with Unknown Parameters and Actuator Faults

Liu

Wang

2023

Mathematical Problems in Engineering

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In this paper, the fault-tolerant lateral control problem is considered for autonomous electric vehicles with unknown parameters and actuator faults. In the real world, the physical characteristics of vehicles are time-varying, causing that the parameters are unknown and even piecewise. Besides, the actuators may encounter with intermittent actuator faults, which can destroy the system’s performance. To deal with the piecewise unknown parameters and actuator faults, quadratic damping terms are designed in the controller and estimators are introduced to estimate those uncertainties. Then, by utilizing the backstepping technique, a novel adaptive fault-tolerant lateral control scheme is proposed, which guarantees that the tracking errors converge into a compact set. Simulation results are provided to validate the effectiveness and robustness of the proposed control scheme.

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“…The modeling of complex multibody system for vehicle dynamics and control is always a challenging task. First, the dynamic tire forces and vehicle-tire-road interactions are difficult to calculate, partially due to external disturbances and parameter perturbation [1][2][3]. Second, the multibody-based formulations lead to full-vehicle models with increased computational burden, which always makes the real-time simulation and control unavailable [4,5].…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms

Nie

Min

Pan

et al. 2022

Sensors

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We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle’s longitudinal-lateral responses. The proposed improved method can restrain the solutions of weight matrices and bias matrices from falling into a local optimum while training the DNN model. First, dynamic simulations for a vehicle are performed based on an efficient semirecursive multibody model for real-time data acquisition. Next, the vehicle data are processed and used to train and test the improved DNN model. The vehicle responses, which are obtained from the LDWPSO-DNN and IWO-DNN models, are compared with the DNN and multibody results. The comparative results show that the LDWPSO-DNN and IWO-DNN models predict accurate longitudinal-lateral responses in real-time without falling into a local optimum. The improved DNN model based on optimization algorithms can be employed for real-time simulation and preview control in intelligent vehicles.

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Adaptive backstepping robust tracking control for stabilizing lateral dynamics of electric vehicles with uncertain parameters and external disturbances

Cited by 29 publications

References 31 publications

In-wheel motor control system used by four-wheel drive electric vehicle based on whale optimization algorithm-proportional–integral–derivative control

In-wheel motor control system used by four-wheel drive electric vehicle based on whale optimization algorithm-proportional–integral–derivative control

Adaptive Fault‐Tolerate Lateral Control for Autonomous Electric Vehicles with Unknown Parameters and Actuator Faults

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms

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