Optimal Trajectory Tracking of Uncertain Nonlinear Continuous-Time Strict-Feedback Systems with Dynamic Constraints

Abstract

A novel method for optimal trajectory tracking of uncertain nonlinear systems by using adaptive dynamic programming (ADP)-based integral reinforcement learning (IRL) and neural networks (NNs) is proposed. The method utilises an actor-critic framework with optimal backstepping to minimise a discounted value function and uses a single-layer NN identifier for estimating the unknown dynamics. For safety assurance, a time-varying barrier Lyapunov function (TVBLF) is used in the control design to handle the constraints. A novel weight-tuning law by using the control input error, integral Bellman error, and the NN identifier is used for the actor-critic framework. A novel lifelong learning (LL)-based method for critic NN is utilised in an optimal framework by incorporating the Bellman error to mitigate catastrophic forgetting in multitasking scenarios. Stability analysis using Lyapunov stability theory is obtained for the overall closed-loop system. Simulations of leader-follower mobile robot formation control show a 25% reduction in the cost in multitasking scenarios.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

lifelong learning; neural networks; nonlinear systems; Optimal control; reinforcement learning; strict feedback

International Standard Serial Number (ISSN)

1366-5820; 0020-7179

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Taylor and Francis Group; Taylor and Francis, All rights reserved.

Publication Date

01 Jan 2024

Link to Full Text

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