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Optimal tuning of multi-PID controller using improved CMOCSO algorithm.

Ying Hu1, Xiongyan Liu1, Hao Chen1

  • 1School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, Wanbailin District, China.

Peerj. Computer Science
|December 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an improved swarm optimizer to tune multi-PID controllers, significantly reducing synchronization errors and boosting interference resistance for better system performance.

Keywords:
CMOCSO algorithmConstrained multi-objective optimizationPID controlSimulationSynchronous control

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Area of Science:

  • Control Systems Engineering
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • Multi-PID controller systems often suffer from synchronization errors and interference.
  • Optimizing controller parameters is crucial for robust performance and stability.

Purpose of the Study:

  • To enhance the performance of multi-PID controllers by minimizing synchronization errors.
  • To improve the interference resistance of multi-PID control systems.
  • To apply an advanced optimization algorithm for parameter tuning.

Main Methods:

  • Formulated a mathematical model for the constrained multi-objective optimization problem of multi-PID controllers.
  • Developed an improved Competitive and Cooperative Swarm Optimizer (CMOCSO) with a central point-moving strategy and novel grouping strategy.
  • Validated the CMOCSO algorithm on 16 standard benchmark functions.

Main Results:

  • The improved CMOCSO algorithm demonstrated effectiveness in solving constrained multi-objective problems.
  • Optimizing multi-PID controller parameters using the proposed method led to superior control performance.
  • Significant reduction in synchronization errors and enhanced interference resistance were observed.

Conclusions:

  • The proposed CMOCSO-based parameter optimization is effective for multi-PID controllers.
  • The method offers a viable solution for improving control system robustness and accuracy.
  • This approach enhances synchronization and interference resilience in complex control systems.