Fault-Tolerant Predictive Control With Deep-Reinforcement-Learning-Based Torque Distribution for Four In-Wheel Motor Drive Electric Vehicles

Abstract

This paper proposes a fault-tolerant control (FTC) method for four in-wheel motor drive electric vehicles considering both vehicle stability and motor power consumption. First, a seven degrees-of-freedom vehicle nonlinear model integrating motor faults is built to design a hierarchical FTC control scheme. The control structure is composed of two levels: an upperlevel nonlinear model predictive controller and a lower-level fault-tolerant coordinated controller. The upper-level controller provides an appropriate reference in terms of additional yaw moment and vehicle longitudinal force, required for vehicle stability control, to the lower-level controller. This latter aims at distributing the four-wheel torques taking into account both vehicle stability and power consumption. Specifically, the weighting factor involved in the optimization-based design of the lower-level controller is determined online by the randomized ensembled double Q−learning reinforcement learning algorithm to achieve an optimal control strategy for the whole vehicle operating range. Moreover, the tradeoff between vehicle stability and power consumption is analyzed, and the necessity of using reinforcement learning is discussed. Numerical experiments are performed under various driving scenarios with a high-fidelity CarSim vehicle model to demonstrate the effectiveness of the proposed control method. Via a comparative study, we highlight the advantages of the new FTC control method over many related existing control results in terms of improving the vehicle stability and driver comfort, as well as reducing the power consumption.

Robust $H_\infty$ Fault-Tolerant Observer-Based PID Path Tracking Control of Autonomous Ground Vehicle With Control Saturation

“…which are still bilinear matrices of design parameters P , P , K, and L. Therefore, it is not easy to solve BMI in (25) for P , P , K, and L. By the fact that [30]…”

“…The actuator fault signal can be detected by a model-based fault diagnosis method in [23] and can be eliminated by the disturbance observer-based FTC for electric vehicle [24]. Besides, the trade-off issue between vehicle stability and power consumption in FTC is tackled by the randomized ensemble double Q-learning deep reinforcement learning algorithm [25]. The state estimation problem of AGV system in controller area network with taking into account the impact of fault signal due to saturation effects from wireless communication characteristics is considered in [26].…”

“…The state estimation problem of AGV system in controller area network with taking into account the impact of fault signal due to saturation effects from wireless communication characteristics is considered in [26]. However, the previous studies often rely on a complicated machine learning or deep learning algorithm in [23], [25] or algebraic equation constraints for designing the fault observer in [24] among the previous researches.…”

Direct Yaw Moment Control for Distributed Drive Electric Vehicles Based on Hierarchical Optimization Control Framework

“…Electric vehicles contribute to reducing environmental pollution and achieve carbon peaking and carbon neutrality goals. Four-wheel distributed drive electric vehicles (4W-DDEVs) adopt a controllable and independent drive motor mounted directly in or near the driving wheel, which has the advantages of quick response, high transmission efficiency, precise control and compact structure [1][2][3]. Thus, 4W-DDEVs are considered as one of the best carriers for vehicle dynamics control technology and have attracted significant attention.…”

Back Propagation Neural Network-Based Fault Diagnosis and Fault Tolerant Control of Distributed Drive Electric Vehicles Based on Sliding Mode Control-Based Direct Yaw Moment Control

“…Guo et al proposed an adaptive fault-tolerant control method based on nonlinear vehicle dynamics and a new quadratic spacing strategy, which is used to ensure vehicle stability [18]. Deng et al proposed a fault-tolerant control method based on deep reinforcement learning to consider the vehicle's stability and the motor's power consumption [19]. Logic-based methods depend more on experts as they require much practical experience.…”