Event-triggered optimal control of completely unknown nonlinear systems via identifier-critic learning

Zhinan Peng; Zhiquan Zhang; Rui Luo; Yiqun Kuang; Jiangping Hu; Hong Cheng; Bijoy Kumar Ghosh

Kybernetika (2023)

  • Volume: 59, Issue: 3, page 365-391
  • ISSN: 0023-5954

Abstract

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This paper proposes an online identifier-critic learning framework for event-triggered optimal control of completely unknown nonlinear systems. Unlike classical adaptive dynamic programming (ADP) methods with actor-critic neural networks (NNs), a filter-regression-based approach is developed to reconstruct the unknown system dynamics, and thus avoid the dependence on an accurate system model in the control design loop. Meanwhile, NN adaptive laws are designed for the parameter estimation by using only the measured system state and input data, and facilitate the identifier-critic NN design. The convergence of the adaptive laws is analyzed. Furthermore, in order to reduce state sampling frequency, two kinds of aperiodic sampling schemes, namely static and dynamic event triggers, are embedded into the proposed optimal control design. Finally, simulation results are presented to demonstrate the effectiveness of the proposed event-triggered optimal control strategy.

How to cite

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Peng, Zhinan, et al. "Event-triggered optimal control of completely unknown nonlinear systems via identifier-critic learning." Kybernetika 59.3 (2023): 365-391. <http://eudml.org/doc/299493>.

@article{Peng2023,
abstract = {This paper proposes an online identifier-critic learning framework for event-triggered optimal control of completely unknown nonlinear systems. Unlike classical adaptive dynamic programming (ADP) methods with actor-critic neural networks (NNs), a filter-regression-based approach is developed to reconstruct the unknown system dynamics, and thus avoid the dependence on an accurate system model in the control design loop. Meanwhile, NN adaptive laws are designed for the parameter estimation by using only the measured system state and input data, and facilitate the identifier-critic NN design. The convergence of the adaptive laws is analyzed. Furthermore, in order to reduce state sampling frequency, two kinds of aperiodic sampling schemes, namely static and dynamic event triggers, are embedded into the proposed optimal control design. Finally, simulation results are presented to demonstrate the effectiveness of the proposed event-triggered optimal control strategy.},
author = {Peng, Zhinan, Zhang, Zhiquan, Luo, Rui, Kuang, Yiqun, Hu, Jiangping, Cheng, Hong, Ghosh, Bijoy Kumar},
journal = {Kybernetika},
keywords = {optimal control; unknown nonlinear system; adaptive dynamic programming; identifier-critic neural networks; event-triggered mechanism},
language = {eng},
number = {3},
pages = {365-391},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Event-triggered optimal control of completely unknown nonlinear systems via identifier-critic learning},
url = {http://eudml.org/doc/299493},
volume = {59},
year = {2023},
}

TY - JOUR
AU - Peng, Zhinan
AU - Zhang, Zhiquan
AU - Luo, Rui
AU - Kuang, Yiqun
AU - Hu, Jiangping
AU - Cheng, Hong
AU - Ghosh, Bijoy Kumar
TI - Event-triggered optimal control of completely unknown nonlinear systems via identifier-critic learning
JO - Kybernetika
PY - 2023
PB - Institute of Information Theory and Automation AS CR
VL - 59
IS - 3
SP - 365
EP - 391
AB - This paper proposes an online identifier-critic learning framework for event-triggered optimal control of completely unknown nonlinear systems. Unlike classical adaptive dynamic programming (ADP) methods with actor-critic neural networks (NNs), a filter-regression-based approach is developed to reconstruct the unknown system dynamics, and thus avoid the dependence on an accurate system model in the control design loop. Meanwhile, NN adaptive laws are designed for the parameter estimation by using only the measured system state and input data, and facilitate the identifier-critic NN design. The convergence of the adaptive laws is analyzed. Furthermore, in order to reduce state sampling frequency, two kinds of aperiodic sampling schemes, namely static and dynamic event triggers, are embedded into the proposed optimal control design. Finally, simulation results are presented to demonstrate the effectiveness of the proposed event-triggered optimal control strategy.
LA - eng
KW - optimal control; unknown nonlinear system; adaptive dynamic programming; identifier-critic neural networks; event-triggered mechanism
UR - http://eudml.org/doc/299493
ER -

References

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