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

Jiaxin Zhang; Kewen Li; Yongming Li

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

机译：基于输出反馈的简化优化的反馈系统，具有输入和状态约束的严格反馈系统

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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.

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《自动化学报（英文版）》 |2021年第6期|1119-1132|共14页
作者
Jiaxin Zhang; Kewen Li; Yongming Li;
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作者单位

College of Science Liaoning University of Technology Jinzhou 121001 China;

Institute of Automation Qufu Normal University Qufu 273165 China;

College of Science Liaoning University of Technology Jinzhou 121001 China;

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

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