A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

• 作者: Behzad Farzanegan Mohsen Zamani Amir Abolfazl Suratgar Mohammad Bagher Menhaj
• 来源期刊: 控制理论与技术（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: RESEARCH ARTICLES
• 页码范围: 283-294
• 页数: 12页
• 语种: 英文

摘要

In this study,an adaptive neuro-observer-based optimal control (ANOPC) policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton-Jacobi-Bellman (HJB) framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and alge-braic parts.In the analytical part,first,an observer-based neural network (NN) approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness (UUB) of all signals of the whole closed-loop system is ensured through Lyapunov's direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.