Nature-Inspired Metaheuristic Optimization for Control Tuning of Complex Systems.

Area of Science:

• Control Systems Engineering
• Computational Intelligence
• Optimization Techniques

Background:

• Tuning controllers for complex dynamic systems, including electromechanical and wind turbine systems, presents significant challenges.
• Meta-heuristic optimization algorithms offer promising approaches for addressing these complex tuning problems.

Purpose of the Study:

• To propose and evaluate a methodology for optimal controller tuning using bio-inspired meta-heuristic techniques.
• To compare the performance of the Antlion Optimizer (ALO) and Whale Optimization Algorithm (WOA) for tuning Proportional-Integral-Derivative (PID) controllers.

Main Methods:

• Application of Antlion Optimizer (ALO) and Whale Optimization Algorithm (WOA) to two distinct dynamic systems: a Hoop & Ball electromechanical system and a Wind Turbine-Generator-Rectifier.
• Evaluation of controller tuning performance based on parameters like the number of agents (nS) and maximum iterations (nMaxIter).
• Assessment of the repeatability and computational effort associated with ALO and WOA implementation.

Main Results:

• Both ALO and WOA achieved comparable results in tuning PID controllers for both the linearized electromechanical system and the complex non-linear wind turbine system.
• The performance of both algorithms was analyzed concerning the number of agents and iterations, with repeatability also considered.
• Computational effort for implementing ALO and WOA was evaluated.

Conclusions:

• The Antlion Optimizer (ALO) and Whale Optimization Algorithm (WOA) are effective and comparable methods for optimal controller tuning in dynamic systems.
• These meta-heuristic techniques can be complementary, allowing for the exploitation of individual algorithm strengths in controller design.
• The findings support the use of ALO and WOA for optimizing conventional PID controllers in various engineering applications.