Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints  

作　　者：Jiaxin Zhang Kewen Li Yongming Li 

机构地区：[1]College of Science,Liaoning University of Technology,Jinzhou 121001,China [2]Institute of Automation,Qufu Normal University,Qufu 273165,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2021年第6期1119-1132,共14页自动化学报（英文版）

基　　金：This work was supported by National Natural Science Foundation of China(61822307,61773188).

摘　　要：In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.

关 键 词：Backstepping design immeasurable states neuralnetworks(NNs) optimal control state constraints 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]