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Improved Particle Swarm Optimization Based on Entropy and Its Application in Implicit Generalized Predictive Control.

Jinfang Zhang1, Yuzhuo Zhai1, Zhongya Han1

  • 1School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.

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|January 21, 2022
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Summary
This summary is machine-generated.

This study introduces an improved particle swarm optimization (PSO) for industrial control systems. The enhanced PSO algorithm effectively reduces system overshoot and settling time, improving control performance under constraints.

Keywords:
implicit generalized predictive controlinertia weightparticle swarm optimizationreverse optimization strategysystem entropy

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Area of Science:

  • Control Systems Engineering
  • Optimization Algorithms
  • Industrial Automation

Background:

  • Industrial systems often face challenges with input-output constraints.
  • Existing particle swarm optimization (PSO) algorithms can suffer from premature convergence and slow operation.

Purpose of the Study:

  • To develop an improved particle swarm optimization (PSO) algorithm for implicit generalized predictive control (IGPC).
  • To enhance the performance of IGPC in handling input-output constraints in industrial systems.

Main Methods:

  • An improved PSO algorithm incorporating system entropy (SR) for a novel weight attenuation strategy and local jump-out mechanism.
  • Modification of the velocity update mechanism and iterative adjustments to prevent local optimization.
  • Integration of the improved PSO with gradient optimization for a rolling-horizon approach.

Main Results:

  • The improved PSO algorithm effectively optimizes the performance index in predictive control.
  • Simulation results demonstrate a reduction in system overshoot by approximately 7.5%.
  • Settling time was reduced by approximately 6% compared to the standard PSO-IGPC.

Conclusions:

  • The proposed improved PSO-IGPC algorithm offers superior performance in managing industrial system constraints.
  • The enhanced optimization strategies significantly improve control accuracy and efficiency.
  • This approach provides a robust solution for complex industrial control applications.