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Model predictive control based on reduced order models applied to belt conveyor system.

Wei Chen1, Xin Li2

  • 1Department of Automation, Hefei University of Technology, 93 Tunxi Road, Hefei 230009, China..

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|September 21, 2016
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Summary

This study introduces a reduced-order model predictive controller (MPC) for complex belt conveyor systems. The method effectively simplifies the model for better control performance and accuracy.

Keywords:
Balanced truncationBelt conveyor systemModel predictive controlModel reduction

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Area of Science:

  • Control Systems Engineering
  • Mechanical Engineering
  • Applied Mathematics

Background:

  • Belt conveyor systems are complex electro-mechanical systems with long, viscoelastic bodies.
  • Controlling these systems requires sophisticated methods to manage their dynamics.

Purpose of the Study:

  • To propose a model predictive controller (MPC) based on a reduced-order model for belt conveyor systems.
  • To enhance control accuracy and system performance by addressing model reduction errors.

Main Methods:

  • Balanced truncation method for belt conveyor model order reduction.
  • Model predictive control (MPC) algorithm utilizing the reduced-order model.
  • Implementation of two Kalman state estimators to compensate for model reduction errors.

Main Results:

  • Balanced truncation significantly reduces model order while maintaining high accuracy.
  • The proposed MPC with the reduced-order model demonstrates effective control of the belt conveyor system.
  • Kalman state estimators improved overall system performance by mitigating model discrepancies.

Conclusions:

  • Reduced-order modeling is a viable approach for controlling complex belt conveyor systems.
  • MPC combined with reduced-order models and Kalman estimators offers a robust control solution.
  • The proposed method achieves accurate and efficient control of belt conveyor dynamics.