Neural Output Feedback Control of Automobile Steer-by-Wire System With Predefined Performance and Composite Learning

• Published in: IEEE transactions on vehicular technology Vol. 72; no. 5; pp. 5906 - 5921
• Main Authors: Wang, Yunlong, Liu, Yan, Wang, Yongfu, Chai, Tianyou
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: adaptive neural network; Angular velocity; Automobiles; composite learning; Control systems design; Controllers; Disturbance observers; Drive by wire; Feedback control; Friction; Learning; Mathematical analysis; Neural networks; Output feedback; prescribed performance; Steady-state; Steer-by-wire (SbW); Steering; Torque; Tracking errors; Transient analysis; Uncertainty; Wheels
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3233621

This article addresses the steering control problem for steer-by-wire (SbW) systems subject to the unknown uncertainty, external disturbance and unavailable variable. Before the controller design, an adaptive neural network-based observer and a disturbance observer are constructed to estimate the angular velocity signal and the compound disturbance, respectively. Then, to guarantee the transient and steady-state performance of steering tracking error within the quantitative boundary, a prescribed performance function is constructed by user-designed tracking accuracy and settling time. Finally, the controller is designed based on the backstepping scheme and the neural network with a composite learning scheme is proposed for the approximation of lumped uncertainty. The Lyapunov stability theory shows that the signals involved in the system are semi-global uniformly ultimately bounded and the tracking error converges to a preset range at finite time. Different numerical simulations and experiments are implemented to verify the effectiveness of the developed control scheme.