Robust LPV Fault Tolerant Tracking Control Using Set-Based Approaches for Autonomous Vehicles

This article proposes a zonotopic filtering-based active fault tolerant tracking control (FTTC) scheme for autonomous vehicles, modeled as linear parameter-varying (LPV) systems subject to disturbances, noise and actuator faults. A zonotopic Kalman filter (ZKF) is proposed to simultaneously estimate both the unmeasurable states and actuator faults. Then, an FTTC is developed, including a reference input, a state-feedback controller, and a fault compensation mechanism. To minimize the impact of uncertainties on the estimates, the optimal filter gain is designed by minimizing the Frobenius norm of the state bounding zonotope. To reduce the tracking error and attenuate the disturbance effects, the controller design ensures the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr{H}_{\infty}$</tex-math></inline-formula> performance of the uncertain tracking error dynamics by constructing a zonotopic Lyapunov function. Finally, the proposed scheme is applied to vehicle lateral dynamics for reference path-tracking control in the presence of actuator faults, and its effectiveness is demonstrated through simulations using real collected data.