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Synergetic learning structure-based neuro-optimal fault tolerant control for unknown nonlinear systems.

Hongbing Xia1, Bo Zhao2, Ping Guo2

  • 1School of Systems Science, Beijing Normal University, Beijing 100875, China; School of Electronic and Electrical Engineering, Bengbu University, Bengbu 233030, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 4, 2022
PubMed
Summary

A new neuro-optimal fault tolerant control method uses synergetic learning structure for unknown nonlinear systems. This approach addresses actuator failures by treating them as a differential game, ensuring system stability.

Keywords:
Adaptive dynamic programmingFault tolerant controlNeural networksSynergetic learningZero-sum games

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Area of Science:

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

Background:

  • Actuator failures pose significant challenges in controlling unknown nonlinear systems.
  • Existing fault tolerant control (FTC) methods often struggle with system uncertainties and complex dynamics.
  • Game theory offers a framework for analyzing and designing controllers for systems with competing objectives or uncertainties.

Purpose of the Study:

  • To propose a novel synergetic learning structure-based neuro-optimal fault tolerant control (SLSNOFTC) method.
  • To address the control problem for unknown nonlinear continuous-time systems susceptible to actuator failures.
  • To guarantee asymptotic convergence of identification errors, controller weights, and system states.

Main Methods:

  • Utilizing a synergetic learning structure (SLS) to decompose the control problem into optimal control input and actuator failure subsystems.
  • Formulating the fault tolerant control problem as a two-player zero-sum differential game based on game theory.
  • Employing a radial basis function neural network-based identifier for unknown system dynamics and an asymptotically stable critic neural network (ASCNN) to solve the Hamilton-Jacobi-Isaacs equation.

Main Results:

  • The proposed SLSNOFTC method effectively identifies unknown system dynamics using input/output data.
  • The ASCNN, with cooperative adaptive tuning laws, ensures the Hamilton-Jacobi-Isaacs equation is solved.
  • Lyapunov stability analysis confirms asymptotic convergence of identification errors, ASCNN weight errors, and all closed-loop system signals.

Conclusions:

  • The developed SLSNOFTC method provides a robust and effective solution for fault tolerant control in unknown nonlinear systems with actuator failures.
  • The approach guarantees superior stability performance compared to methods only ensuring uniform ultimate boundedness.
  • Numerical simulations validate the high effectiveness and reliability of the proposed neuro-optimal fault tolerant control strategy.