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    Area of Science:

    • Control Systems Engineering
    • Computational Intelligence
    • Electrical Engineering

    Background:

    • Conventional proportional-integral-derivative (PID) controllers face limitations when adapted with functions like Gaussian, leading to complex parameter tuning.
    • Optimizing the nine free parameters of a Gaussian adaptive PID (GAPID) controller often lacks algebraic solutions, necessitating advanced optimization techniques.

    Purpose of the Study:

    • To propose and compare two bio-inspired metaheuristics, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), for tuning the parameters of a GAPID controller.
    • To link GAPID controller parameters to linear PID gains, maintaining similar design requirements.
    • To evaluate the effectiveness of GA and PSO in optimizing GAPID controller performance for a step-down DC-DC converter.

    Main Methods:

    • Employing Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to determine the optimal nine parameters for the GAPID controller.
    • Linking the GAPID controller's Gaussian function parameters to the linear PID gains.
    • Utilizing a step-down DC-DC converter model, a second-order system, for simulation and experimental validation.

    Main Results:

    • Both GA and PSO successfully optimized the GAPID controller parameters, achieving effective control.
    • Particle Swarm Optimization (PSO) exhibited advantages in terms of implementation simplicity and reduced computational load compared to Genetic Algorithms.
    • Simulations and experimental results confirmed the successful application of both metaheuristics.

    Conclusions:

    • PSO is a highly effective and efficient metaheuristic for tuning GAPID controllers, offering a practical alternative to GA.
    • The proposed method of linking GAPID parameters to PID gains simplifies the design process without compromising control performance.
    • The study validates the use of metaheuristics for optimizing adaptive control systems in power electronics applications.