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An optimal tracking neuro-controller for nonlinear dynamic systems.

Y M Park1, M S Choi, K Y Lee

  • 1Dept. of Electr. Eng., Seoul Nat. Univ.

IEEE Transactions on Neural Networks
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal tracking neuro-controller (OTNC) for nonlinear systems. The novel controller effectively manages both steady-state and transient outputs, demonstrating strong performance in simulations.

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Designing effective controllers for discrete-time nonlinear dynamic systems remains a challenge.
  • Existing methods may struggle to optimize performance for complex systems with quadratic cost functions.

Purpose of the Study:

  • To develop an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems.
  • To improve control over both steady-state and transient outputs using a multilayer neural network approach.

Main Methods:

  • The OTNC comprises a feedforward neuro-controller (FFNC) and a feedback neuro-controller (FBNC).
  • FFNC design utilizes a novel inverse mapping concept with a neuro-identifier.
  • FBNC training employs a generalized backpropagation-through-time (GBTT) algorithm to minimize a quadratic cost function.

Main Results:

  • The proposed FFNC effectively controls the steady-state output of the plant.
  • The FBNC, trained with GBTT, successfully manages the transient-state output.
  • A case study on a nonlinear plant demonstrated the good performance of the developed OTNC.

Conclusions:

  • The developed OTNC offers an effective off-line control strategy for nonlinear dynamic systems.
  • The methodology provides a robust approach for designing controllers by first identifying the plant and then designing the controller.
  • The integration of FFNC and FBNC within the OTNC framework enhances control precision for complex systems.