Finite-time optimized robust control with adaptive state estimation algorithm for autonomous heavy vehicle

Abstract

Structured and unstructured uncertainties coupled with external disturbances usually pose considerable challenges in realizing heavy vehicle automatic lane guidance control. This paper presents a novel solution paradigm for the lateral control of autonomous heavy vehicle. The proposed control framework consists of an adaptive state estimator and a finite-time optimized ℋ∞ state feedback regulator. Firstly, based on the dual extended Kalman filter (DEKF) technique, which can estimate model time-varying parameters and unknown states at the same time, the adaptive state estimation algorithm is proposed and investigated to improve state estimation adaptivity against uncertain time-varying general front/rear axle cornering stiffnesses. Then, using zero-sum dynamic game theory as the general framework, we analyze the minimax controller design problem and introduce the finite-time optimized robust regulator design method. The autonomous heavy vehicle automatic lane guidance controller is designed in the framework of robust optimal state feedback control to make it robust against external disturbance. Finally, results of various driving scenarios from both simulation and hardware-in-loop (HIL) implementation show that the proposed finite-time optimized robust and adaptive state estimation control paradigm can robustly obtain satisfactory automated lateral control performance for heavy vehicle when the control system is characterized by both time-varying model parameters and uncertain external energy-bounded disturbance. A comparative study is also conducted to investigate cases to show its effectiveness when the adaptive state estimation is used and when it is not.