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Local Tracking Control for Unknown Interconnected Systems via Neuro-Dynamic Programming

  • Bo Zhao
  • Derong Liu
  • Mingming Ha
  • Ding Wang
  • Yancai Xu
  • Qinglai Wei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

Abstract

This paper develops a neuro-dynamic programming based local tracking control (LTC) scheme for unknown interconnected systems. By using the local input-output data and the desired states of coupling subsystems, a local neural network (NN) identifier is established to obtain the local input gain matrix online. By introducing a modified local cost function, the Hamilton-Jacobi-Bellman equation is solved by a local critic NN with asymptotically convergent weight vector, which is obtained by nested update law, and the LTC can be derived with the desired state aided augmented subsystem. The stability of the closed-loop system is shown by Lyapunov’s direct method. The simulation on the parallel inverted pendulum system illustrates that the developed LTC scheme is effective.

Keywords

Adaptive dynamic programming Neuro-dynamic programming Local tracking control Optimal control Unknown interconnected systems 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61603387, 61773075, 61533017 and 61773373, and in part by the Early Career Development Award of SKLMCCS under Grant 20180201.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Bo Zhao
    • 1
  • Derong Liu
    • 2
  • Mingming Ha
    • 3
  • Ding Wang
    • 1
  • Yancai Xu
    • 1
  • Qinglai Wei
    • 1
  1. 1.The State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.School of AutomationGuangdong University of TechnologyGuangzhouChina
  3. 3.School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijingChina

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