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Output information-based intermittent optimal control for continuous-time nonlinear systems with unmatched

Weifeng Wang1, Heping Gu2, Jun Mei3

  • 1School of Mathematics and Statistics, South-Central Minzu University, Wuhan 430074, China.

ISA Transactions
|February 17, 2024
PubMed
Summary

This study introduces robust optimal intermittent control for nonlinear systems with uncertainties. The novel approach ensures finite-time stability and outperforms existing methods.

Keywords:
Adaptive dynamic programmingFinite-time stabilizationNeural networksNonlinear input-affine systemsOutput information-based intermittent controlUnmatched uncertainties

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Area of Science:

  • Control Systems Engineering
  • Nonlinear Dynamics
  • Artificial Intelligence in Control

Background:

  • Intermittent control offers resource savings but faces challenges in nonlinear systems with uncertainties.
  • Existing methods struggle with robust optimal intermittent control for input-affine nonlinear systems.

Purpose of the Study:

  • To develop a robust optimal intermittent control strategy for nonlinear input-affine systems with unmatched uncertainties.
  • To ensure finite-time stability and improve resource conservation in complex dynamic systems.

Main Methods:

  • Utilizing a neural networks (NNs) state observer for system information estimation.
  • Employing the Hamilton-Jacobi-Bellman (HJB) methodology for finite-time optimal intermittent control.
  • Developing an event-triggered intermittent (ETI) approach using robust adaptive dynamic programming (ADP) with NNs.

Main Results:

  • The proposed enhanced finite-time intermittent control approach guarantees system stability.
  • A novel optimal intermittent control law is derived using robust adaptive dynamic programming.
  • Simulation results demonstrate the superiority of the proposed method over existing strategies.

Conclusions:

  • The developed robust optimal intermittent control strategy effectively addresses challenges in nonlinear systems.
  • The integration of NNs, HJB, and ADP provides a powerful framework for advanced control.
  • The ETI approach offers significant improvements in performance and resource efficiency.