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

  • Ding Wang
  • Chaoxu Mu
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 167)

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.The State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.School of Electrical and Information EngineeringTianjin UniversityTianjinChina

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