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Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

Ding Wang1, Derong Liu2, Yun Zhang2

  • 1The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 15, 2017
PubMed
Summary

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This summary is machine-generated.

This study introduces a neural network control method for nonlinear systems with uncertainties. It achieves robust tracking without needing an initial stabilizing controller, verified by theory and simulation.

Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Nonlinear System Dynamics

Background:

  • Nonlinear systems present challenges in robust tracking control due to uncertainties.
  • Adaptive critic techniques offer a promising approach for optimal control design.
  • Existing methods often require an initial stabilizing controller, limiting their applicability.

Purpose of the Study:

  • To develop a neural-network-based robust tracking control scheme for nonlinear systems with matched uncertainties.
  • To eliminate the need for an initial stabilizing controller in the control design process.
  • To ensure theoretical and practical validation of the proposed control strategy.

Main Methods:

  • Formulation of an augmented system incorporating tracking error and reference trajectory.
Keywords:
Adaptive critic designsDynamical uncertaintyLearning systemsNeural networksOptimal controlRobust tracking control

Related Experiment Videos

  • Application of adaptive critic optimal control principles to the augmented system.
  • Derivation of an approximate control law by solving the Hamilton-Jacobi-Bellman equation.
  • Closed-loop stability analysis using the Lyapunov approach.

    • Main Results:

    • A novel neural-network-based robust tracking control scheme was successfully established.
    • The proposed method effectively handles nonlinear systems with matched uncertainties.
    • The absence of an initial stabilizing controller requirement was demonstrated.
    • Theoretical guarantees for robust tracking performance were provided.

    Conclusions:

    • The adaptive critic technique, combined with neural networks, provides an effective solution for robust tracking control in nonlinear systems.
    • The developed control scheme offers a practical and theoretically sound approach for systems with uncertainties.
    • Simulation results confirm the efficacy and robustness of the proposed control strategy.