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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Robust shrinking ellipsoid model predictive control for linear parameter varying system.

Zhang Longge1, Yan Yan1

  • 1Department of Mathematics and Physics, North China Electric Power University, Baoding, PR China.

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Summary
This summary is machine-generated.

A new off-line model predictive control strategy effectively stabilizes linear parameter varying systems with polytopic uncertainty. This method reduces online computations by solving most problems offline, ensuring system stability and demonstrating practical effectiveness.

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Area of Science:

  • Control Theory
  • Systems Engineering

Background:

  • Linear parameter varying (LPV) systems are common in engineering.
  • Polytopic uncertainty presents challenges for robust control design.
  • Model predictive control (MPC) offers powerful control capabilities.

Purpose of the Study:

  • To develop a novel off-line model predictive control (MPC) strategy.
  • To address control of LPV systems with polytopic uncertainty.
  • To reduce computational burden during real-time operation.

Main Methods:

  • Constructing a nest of shrinking ellipsoids using linear matrix inequality (LMI).
  • Splitting the MPC objective function into off-line and on-line components.
  • Ensuring system convergence to a smaller ellipsoid through on-line control calculation.

Main Results:

  • The proposed strategy successfully moves the majority of computations off-line.
  • Stability of the closed-loop system is rigorously proven.
  • Numerical examples validate the effectiveness of the developed control method.

Conclusions:

  • The novel off-line MPC strategy provides an efficient and stable solution for uncertain LPV systems.
  • Reduced on-line computation enhances practical applicability.
  • The method demonstrates significant potential for real-world control applications.