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Observer-Based Online Adaptive Regulation for a Class of Uncertain Nonlinear Systems

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Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 167))

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Abstract

A novel observer-based online control strategy is proposed for a class of uncertain continuous-time nonlinear systems based on solving the HJB equation. Due to the dynamics complexity, the approximate optimal control for affine uncertain continuous-time nonlinear systems is pursued by policy iteration algorithm. Considering that only output variables can be measured in control practice, an observer is designed to reconstruct all system states by relying on output information and then is used to develop the policy iteration control scheme. The observer-based policy iteration algorithm can approximately solve the HJB equation within the ADP framework, where a critic neural network is constructed to approximate the optimal cost function. Then, the approximate expression of the optimal control policy can be directly derived from solving the HJB equation. Additionally, the stability of the closed-loop system is provided based on the Lyapunov theory. Two simulation examples are presented to verify the effectiveness of the proposed control approach.

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Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ding Wang

  2. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Chaoxu Mu

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  1. Ding Wang
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  2. Chaoxu Mu
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Correspondence to Ding Wang .

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Wang, D., Mu, C. (2019). Observer-Based Online Adaptive Regulation for a Class of Uncertain Nonlinear Systems. In: Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems. Studies in Systems, Decision and Control, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-13-1253-3_3

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