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Controller optimization using data-driven constrained bat algorithm with gradient-based depth-first search strategy.

Hu Li1, Bao Song1, Xiaoqi Tang1

  • 1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.

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Summary
This summary is machine-generated.

This study introduces a data-driven bat algorithm for controller optimization, reducing reliance on explicit system models. It effectively solves constrained optimization problems using experimental data and a novel search strategy.

Keywords:
Controller optimizationData-driven constrained bat algorithmDepth-first search strategyServo drive system

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Area of Science:

  • Engineering
  • Computer Science
  • Control Theory

Background:

  • Meta-heuristic algorithms are popular for controller optimization but often require explicit system models.
  • Solving constrained optimization problems (COPs) with these algorithms is challenging when precise models are unavailable.

Purpose of the Study:

  • To propose a novel data-driven constrained bat algorithm for controller optimization.
  • To reduce the dependence on explicit system models by utilizing experimental data.
  • To enhance the performance and convergence of optimization algorithms for COPs.

Main Methods:

  • A gradient-based depth-first search (GDFS) strategy was developed to predetermine a valid search space satisfying system constraints.
  • An improved boundary constraint handling method was implemented to confine the search within the predetermined space.
  • The bat algorithm was combined with an ɛ-constraint-handling method for global optimum seeking, enhanced by elite layer-based local search and social learning-based walk mechanisms.

Main Results:

  • The proposed algorithm successfully solves COPs using experimental data, demonstrating independence from precise system modeling.
  • Enhanced search performance was achieved through mechanisms balancing exploration and exploitation.
  • Convergence was ensured, and effectiveness was validated on a servo drive system and benchmark functions.

Conclusions:

  • The data-driven constrained bat algorithm with GDFS is effective for controller optimization, especially when explicit system models are difficult to obtain.
  • The method offers a robust approach to solving COPs in real-world scenarios.
  • The algorithm provides a valuable tool for optimizing complex systems using experimental data.