Abstract
This paper presents a trajectory tracking error detection method for autonomous vehicles (AVs) via an optimal control scheme, where two online learning algorithms are designed: (i) an adaptive learning algorithm (ALA) and (ii) a finite-time adaptive learning algorithm (FTALA). We first construct an error dynamic system by combining the kinematic equation of AVs and an ideal tracking trajectory. To realize the adaptive optimal control for AVs, the ALA is designed, which provides us with an online solution by resolving the derived Hamilton–Jacobi–Bellman (HJB) equation. Then, the FTALA is presented, which further relaxes the requirement of the system dynamics, i.e., the system drift is not required. The adaptive critic and control action in both online learning algorithms continuously and simultaneously interact, eliminating the need for iterative steps. This approach also avoids the use of an actor neural network (NN) and an initial stabilizing control policy. Moreover, the finite-time convergence can be ensured via adopting a sliding mode technique in the FTALA. Finally, both online learning algorithms are applied to control AVs, and the simulations show their effectiveness and practicality feasibility. The FTALA reduces the time-accumulated tracking error and the cumulative control effort by about 39% and 57% in lane-keeping, and by about 38% and 57% in the S-curve, respectively.