控制理论(社会学)
观察员(物理)
自适应控制
控制器(灌溉)
国家(计算机科学)
趋同(经济学)
自适应系统
控制系统
作者
Yongming Li,Yan-Jun Liu,Shaocheng Tong
出处
期刊:IEEE Transactions on Neural Networks
[Institute of Electrical and Electronics Engineers]
日期:2021-01-26
卷期号:: 1-15
被引量:13
标识
DOI:10.1109/tnnls.2021.3051030
摘要
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.
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