Genetic Algorithms and Evolutionary Computing in Control System Optimization: A Review
Abstract
Control system optimization is crucial for enhancing system performance, efficiency, and robustness across various engineering domains, including robotics, power systems, aerospace, and manufacturing. Traditional optimization techniques, such as gradient-based methods and dynamic programming, often face limitations when dealing with highly complex, nonlinear, and multi-objective control problems. Genetic Algorithms (GAs) and other Evolutionary Computing (EC) techniques, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO), have emerged as powerful tools for addressing these challenges. These techniques leverage principles of natural selection and evolutionary strategies to explore vast search spaces and identify near-optimal solutions efficiently.
This review explores the application of GAs and EC techniques in control system optimization, discussing their methodologies, advantages, and real-world applications in areas like PID controller tuning, model predictive control, and adaptive control systems. Additionally, the paper highlights recent innovations, such as hybrid approaches that integrate EC with traditional methods, AI-driven optimizations, and multi-objective evolutionary algorithms. The potential of these advanced techniques in tackling uncertainty, improving real-time control, and enhancing computational efficiency is also examined. Finally, future research directions, including deep learning-enhanced EC, parallel computing implementations, and quantum-inspired evolutionary algorithms, are discussed to provide insights into the evolving landscape of control system optimization.
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