Neural Network-Based Optimal Tracking Control of Continuous-Time Uncertain Nonlinear System via Reinforcement Learning

摘要

In this note, optimal tracking control for uncertain continuous-time nonlinear system is investigated by using a novel reinforcement learning (RL) scheme. The uncertainty here refers to unknown system drift dynamics. Based on the nonlinear system and reference signal, we firstly formulate the tracking problem by constructing an augmented system. The optimal tracking control problem for original nonlinear system is thus transformed into solving the Hamilton–Jacobi–Bellman (HJB) equation of the augmented system. A new single neural network (NN)-based online RL method is proposed to learn the solution of tracking HJB equation while the corresponding optimal control input that minimizes the tracking HJB equation is calculated in a forward-in-time manner without requiring any value, policy iterations and the system drift dynamics. In order to relax the dependence of the RL method on traditional Persistence of Excitation (PE) conditions, a concurrent learning technique is adopted to design the NN tuning laws. The Uniformly Ultimately Boundedness of NN weight errors and closed-loop augmented system states are rigorous proved. Three numerical simulation examples are given to demonstrate the effectiveness of the proposed scheme.