Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with...

Area of Science:

• Control Systems Engineering
• Computational Intelligence
• Robotics

Background:

• Traditional PID controllers struggle with robustness and performance under varying loads.
• Adaptive control methods often face challenges with abrupt transitions.
• Optimizing adaptive controller parameters lacks a standardized mathematical approach.

Purpose of the Study:

• To compare the effectiveness of two metaheuristic optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in tuning Gaussian Adaptive PID (GAPID) controllers.
• To evaluate the performance, convergence, and solution quality of six variations of GA and PSO for GAPID parameter optimization.
• To demonstrate the enhanced performance and robustness of the optimized GAPID controller for a DC motor with variable load compared to a linear PID.

Main Methods:

• Utilized six variations each of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as bio-inspired optimization algorithms.
• Applied these algorithms to optimize the parameters of a Gaussian Adaptive PID (GAPID) controller, focusing on its concavity and gain bounds.
• Tested the optimized GAPID controller on a DC motor system with a variable load, performing load and gain sweep tests.

Main Results:

• The optimized Gaussian Adaptive PID (GAPID) controllers exhibited fast response times with minimal overshoot.
• Results demonstrated significant robustness to load variations, with minimal performance degradation.
• The metaheuristic optimization techniques (GA and PSO) effectively identified optimal parameters for the GAPID controller.

Conclusions:

• Optimized GAPID controllers significantly outperform traditional linear PID controllers in terms of speed, overshoot, and robustness.
• Metaheuristic algorithms like GA and PSO are effective tools for tuning complex adaptive control systems where mathematical methods are insufficient.
• The proposed GAPID control strategy offers a robust and high-performance solution for systems with dynamic load conditions, such as DC motors.