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Related Experiment Video

Updated: Feb 19, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Two stage neural network modelling for robust model predictive control.

Krzysztof Patan1

  • 1Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.

ISA Transactions
|November 7, 2017
PubMed
Summary

This study introduces a robust model predictive control (MPC) system using artificial neural networks to model plants and uncertainties. The novel approach simplifies optimization for enhanced control system stability and performance.

Keywords:
Neural networksPredictive controlRobustnessStability

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Area of Science:

  • Control Engineering
  • Artificial Intelligence
  • Systems Science

Background:

  • Model Predictive Control (MPC) is a powerful control strategy, but its performance can degrade due to model uncertainties.
  • Artificial Neural Networks (ANNs) offer advanced capabilities for system modeling and adaptation.

Purpose of the Study:

  • To develop a novel robust model predictive control (MPC) scheme using ANNs.
  • To enhance the modeling of plant dynamics and uncertainties within the MPC framework.
  • To ensure the stability and performance of the proposed control system.

Main Methods:

  • Utilizing ANNs for both fundamental plant modeling and uncertainty estimation.
  • Applying instantaneous linearization to simplify the optimization problem.
  • Formulating the optimization as a constrained quadratic programming problem.
  • Analyzing system stability by examining the monotonic decrease of a cost function.

Main Results:

  • A novel robust MPC scheme effectively utilizing ANNs for plant and uncertainty modeling was developed.
  • The instantaneous linearization simplified the optimization process into a constrained quadratic programming problem.
  • System stability was proven through the monotonic decrease of the cost function.
  • The control system demonstrated robust performance on a pneumatic servomechanism across various operating conditions.

Conclusions:

  • The proposed ANN-based robust MPC scheme provides an effective approach for controlling systems with uncertainties.
  • The method ensures stability and simplifies the computational complexity of the MPC optimization.
  • Validation on a pneumatic servomechanism confirms the practical applicability and robustness of the developed control strategy.