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Online control parameter optimization design for multi-machine coordinated loading system of hazardous substances.

Zuoxun Wang1, Chuanyu Cui1, Jinxue Sui1

  • 1School of Information and Electronic Engineering, Shandong Technology and Business University, 264005, China.

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|September 16, 2024
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Summary

A new Full-Scale Smart Parameter Optimization Control (FSPOC) system enhances hazardous material handling stability. This innovative approach uses a bionic fish scale algorithm for superior multi-machine coordination and parameter optimization.

Keywords:
Disturbance cancellation control theoremFish scale prediction algorithmHazardous materialsMulti-machine cooperative systemParameter optimization controller

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Area of Science:

  • Control Systems Engineering
  • Robotics
  • Artificial Intelligence

Background:

  • Parameter instability is a critical issue in hazardous materials handling during multi-machine operations.
  • Existing control methods struggle with the complexity of high-dimensional nonlinear systems in cooperative environments.

Purpose of the Study:

  • To develop a robust control system for enhancing parameter stability in multi-machine hazardous materials handling.
  • To introduce a novel approach for online distributed parameter optimization in complex cooperative systems.

Main Methods:

  • Development of a Full-Scale Smart Parameter Optimization Control (FSPOC) system.
  • Leveraging a novel fish scale prediction algorithm inspired by bionic principles for predictive control.
  • Introduction of a disturbance cancellation control theorem and a parameter optimization controller for enhanced stability.
  • Application to multi-machine cooperative systems with multiple degrees of freedom.

Main Results:

  • The FSPOC system demonstrated superior performance in simulations compared to Genetic Algorithm-based PID (GAPID) and Chaotic Atomic Search Algorithm-based PID (CHASO).
  • The proposed method effectively addressed parameter instability in high-dimensional nonlinear spaces.
  • Online distributed parameter optimization was successfully achieved for complex multi-machine systems.

Conclusions:

  • The FSPOC system offers a significant advancement in achieving stable and optimized control for multi-machine hazardous materials handling.
  • The bionic fish scale prediction algorithm provides a powerful tool for predictive control in cooperative robotic systems.
  • The study validates the superiority of the FSPOC method for complex, high-dimensional control challenges.