Li Yongming, Liu Yanjun, Tong Shaocheng
IEEE Trans Neural Netw Learn Syst. 2022 Jul;33(7):3131-3145. doi: 10.1109/TNNLS.2021.3051030. Epub 2022 Jul 6.
This article proposes an adaptive neural network (NN) output feedback optimized control design for a class of strict-feedback nonlinear systems that contain unknown internal dynamics and the states that are immeasurable and constrained within some predefined compact sets. NNs are used to approximate the unknown internal dynamics, and an adaptive NN state observer is developed to estimate the immeasurable states. By constructing a barrier type of optimal cost functions for subsystems and employing an observer and the actor-critic architecture, the virtual and actual optimal controllers are developed under the framework of backstepping technique. In addition to ensuring the boundedness of all closed-loop signals, the proposed strategy can also guarantee that system states are confined within some preselected compact sets all the time. This is achieved by means of barrier Lyapunov functions which have been successfully applied to various kinds of nonlinear systems such as strict-feedback and pure-feedback dynamics. Besides, our developed optimal controller requires less conditions on system dynamics than some existing approaches concerning optimal control. The effectiveness of the proposed optimal control approach is eventually validated by numerical as well as practical examples.
本文针对一类严格反馈非线性系统，提出了一种自适应神经网络（NN）输出反馈优化控制设计方法。这类系统包含未知内部动态，且状态不可测量并被限制在一些预定义的紧致集内。神经网络用于逼近未知内部动态，并开发了一种自适应神经网络状态观测器来估计不可测量的状态。通过为子系统构造一种障碍型最优代价函数，并采用观测器和行为-评判架构，在反步法技术框架下开发了虚拟和实际最优控制器。除了确保所有闭环信号的有界性外，所提出的策略还能保证系统状态始终被限制在一些预先选定的紧致集内。这是通过障碍李雅普诺夫函数实现的，该函数已成功应用于各种非线性系统，如严格反馈和纯反馈动态系统。此外，与一些现有的最优控制方法相比，我们开发的最优控制器对系统动态的条件要求更少。所提出的最优控制方法的有效性最终通过数值和实际例子得到验证。
