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An approximate internal model-based neural control for unknown nonlinear discrete processes.

Han-Xiong Li1, Hua Deng

  • 1Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong. mehxli@cityu.edu.hk

IEEE Transactions on Neural Networks
|May 26, 2006
PubMed
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A novel neural control strategy effectively manages unknown nonlinear systems, even unstable ones. This approximate internal model-based neural control (AIMNC) offers direct implementation and proven stability for disturbed environments.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Existing neural internal model control methods face limitations with unknown, nonaffine nonlinear discrete processes, especially under disturbed environments.
  • Control of open-loop unstable nonlinear processes or systems with unstable zero dynamics remains a challenge.

Purpose of the Study:

  • To propose a novel approximate internal model-based neural control (AIMNC) strategy for unknown nonaffine nonlinear discrete processes.
  • To address limitations of existing methods by enabling control of unstable systems and simplifying implementation.
  • To ensure analytical stability and robustness of the closed-loop system.

Main Methods:

  • Development of a novel input-output approximation for direct neural control law derivation.

Related Experiment Videos

  • Implementation of a single neural network for model identification and direct control algorithm generation.
  • Analytical derivation of closed-loop system stability and robustness.

    • Main Results:

    • The proposed AIMNC strategy demonstrates superior performance compared to existing methods in simulations.
    • The strategy is effective for open-loop unstable nonlinear processes and systems with unstable zero dynamics.
    • The control law can be derived and implemented straightforwardly for unknown processes.

    Conclusions:

    • The AIMNC strategy provides an effective and robust solution for controlling unknown nonaffine nonlinear discrete processes.
    • The method simplifies the control design process by requiring only one neural network and direct algorithm derivation.
    • AIMNC offers significant advantages for systems previously difficult to control, including unstable ones.