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Model predictive control for systems with fast dynamics using inverse neural models.

Marios Stogiannos1, Alex Alexandridis2, Haralambos Sarimveis3

  • 1Department of Electronic Engineering, Technological Educational Institute of Athens, Agiou Spiridonos, Aigaleo 12243, Greece; School of Chemical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografos 15780, Greece.

ISA Transactions
|October 22, 2017
PubMed
Summary
This summary is machine-generated.

A new model predictive control (MPC) method combines neural control techniques for faster optimization. This robust approach enhances control of nonlinear systems with fast dynamics.

Keywords:
Applicability domainInverse modelsInverted pendulumModel predictive controlNeural networksRadial basis function

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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Nonlinear System Dynamics

Background:

  • Model Predictive Control (MPC) is a powerful control strategy for complex systems.
  • Integrating neural networks into MPC can improve performance but presents challenges.
  • Handling highly nonlinear systems with fast dynamics remains a significant control problem.

Purpose of the Study:

  • To introduce a novel Model Predictive Control (MPC) scheme.
  • To integrate direct and indirect neural control methodologies.
  • To enhance the speed and robustness of MPC for fast, nonlinear systems.

Main Methods:

  • Developed a novel MPC scheme by integrating direct and indirect neural control.
  • Utilized a robust inverse radial basis function (RBF) model.
  • Incorporated the applicability domain criterion for optimizer initialization.
  • Evaluated performance on a highly nonlinear system with fast dynamics.

Main Results:

  • The proposed MPC scheme significantly outperforms existing control methods.
  • The controller solves the optimization problem in less than one sampling period.
  • Demonstrated effective control of a highly nonlinear system with fast dynamics.

Conclusions:

  • The integrated neural-MPC approach offers superior performance for challenging control tasks.
  • The method's speed enables effective control of systems with fast dynamics.
  • This work advances the applicability of MPC to complex, real-world systems.