Approximate Optimal Curve Path Tracking Control for Nonlinear Systems with Asymmetric Input Constraints

Abstract

This paper proposes an approximate optimal curve-path-tracking control algorithm for partially unknown nonlinear systems subject to asymmetric control input constraints. Firstly, the problem is simplified by introducing a feedforward control law, and a dedicated design for optimal control with asymmetric input constraints is provided by redesigning the control cost function in a non-quadratic form. Then, the optimality and stability of the derived optimal control policy is demonstrated. To solve the underlying tracking Hamilton–Jacobi–Bellman (HJB) equation in consideration of partially unknown systems, an integral reinforcement learning (IRL) algorithm is utilized using the neural network (NN)-based value function approximation. Finally, the effectiveness and generalization of the proposed method is verified by experiments carried out on a high-fidelity hardware-in-the-loop (HIL) simulation system for fixed-wing unmanned aerial vehicles (UAVs) in comparison with three other typical path-tracking control algorithms.