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Optimized Model Predictive Controller Using Multi-Objective Whale Optimization Algorithm for Urban Rail Train

Longda Wang1, Lijie Wang1, Yan Chen2

  • 1School of Electrical Engineering, Dalian Jiaotong University, Dalian 116028, China.

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Summary
This summary is machine-generated.

This study introduces an optimized model predictive controller (MPC-MOWOA) for urban rail train control, enhancing energy efficiency and punctuality. The new method improves tracking control and real-time adaptability for automatic train operation systems.

Keywords:
control parametersmodel predictive controllermulti-objective whale optimization algorithmtracking controlurban rail train

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Area of Science:

  • * Urban rail transit engineering
  • * Control systems engineering
  • * Optimization algorithms

Background:

  • * Increasing demands for energy efficiency, comfort, punctuality, and precise stopping in urban rail transit.
  • * Limitations of conventional model predictive control (MPC) and whale optimization algorithms (WOA) in adaptability and robustness.
  • * Need for advanced control strategies for automatic train operation systems.

Purpose of the Study:

  • * To propose an optimized model predictive controller using a multi-objective whale optimization algorithm (MPC-MOWOA) for urban rail train tracking control.
  • * To enhance the adaptability and robustness of the control system for real-time operation.
  • * To validate the performance of the proposed MPC-MOWOA against existing control methods.

Main Methods:

  • * Development of an optimized model predictive controller (MPC-MOWOA) integrating a multi-objective whale optimization algorithm.
  • * Introduction of a nonlinear convergence mechanism and Tchebycheff decomposition for algorithm improvement.
  • * Integration of a fuzzy satisfaction function and online coefficient correction for adaptive real-time control.
  • * Hardware-in-the-loop simulation experiments (HILSEs) on a TMS320F28335 embedded processor.

Main Results:

  • * The MPC-MOWOA significantly reduced the integral of time-weighted absolute error by 39.6% compared to Fuzzy-PID control.
  • * Energy consumption was decreased by 5.9% with the proposed control strategy.
  • * Punctuality, stopping accuracy, and comfort were improved by 33.2%, 12.4%, and 7.1%, respectively.
  • * Demonstrated real-time implementation capability on embedded processors.

Conclusions:

  • * The proposed MPC-MOWOA offers superior performance in urban rail train tracking control.
  • * The enhanced algorithm overcomes limitations of conventional methods, providing better adaptability and robustness.
  • * The study confirms the practical applicability of MPC-MOWOA in real-world urban rail transit systems.