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Data-Driven Suboptimal Scheduling of Switched Systems.

Chi Zhang1, Minggang Gan1, Jingang Zhao1

  • 1State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China.

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|March 4, 2020
PubMed
Summary

This study introduces a data-driven optimal scheduling approach for continuous-time switched systems. The proposed policy iteration algorithm effectively learns optimal switching policies online, even with unknown system dynamics.

Keywords:
continuous timedata-driven controloptimal switchingpolicy iterationswitched systems

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Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Optimization Theory

Background:

  • Continuous-time switched systems are prevalent in various engineering applications.
  • Optimal scheduling is crucial for efficient system operation but challenging with unknown dynamics.
  • Existing methods often require complete system knowledge, limiting their applicability.

Purpose of the Study:

  • To develop a data-driven optimal scheduling approach for continuous-time switched systems with unknown subsystems.
  • To design a policy iteration (PI) based algorithm for online approximation of optimal switching policies.
  • To validate the proposed method's effectiveness using simulation examples.

Main Methods:

  • A policy iteration (PI) based algorithm is proposed for online optimal switching policy approximation.
  • A data-driven PI algorithm utilizes system state data for systems with unknown subsystems.
  • Approximation functions and their weight vectors are employed to estimate policies and cost functions.

Main Results:

  • The proposed algorithms enable online learning of optimal switching policies using only system data.
  • Convergence and performance analyses demonstrate the algorithms' theoretical soundness.
  • Simulation results confirm the effectiveness of the data-driven approach for unknown switched systems.

Conclusions:

  • The developed data-driven optimal scheduling approach is effective for continuous-time switched systems, particularly those with unknown dynamics.
  • The policy iteration-based method offers a viable solution for online policy and cost function approximation.
  • This work contributes to advancing control strategies for complex dynamic systems through data-driven methods.