Lifelong reinforcement learning tracking control of nonlinear strict-feedback systems using multilayer neural networks with constraints

Abstract

This paper presents a novel safe integral reinforcement learning (IRL)-based optimal trajectory tracking scheme for nonlinear systems with uncertain dynamics that is subject to constraints. We leverage multilayer neural networks (MNNs) for actor-critic MNNs along with an NN identifier in the backstepping process for minimizing a discounted value function. A time-varying barrier Lyapunov function (TVBLF) is utilized for handling constraints and to provide safety assurances. Online weight update laws for the actor and critic MNNs are derived that are driven by Bellman error and control input error. We introduce an online lifelong learning (LL) method in the critic NN, utilizing the Bellman error in MNNs to address catastrophic forgetting. The method’s effectiveness is demonstrated through simulations on mobile robot multitask tracking. The paper concludes with a stability analysis of the closed-loop system.