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

This work aims to analyze two metaheuristics optimization techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with six variations each, and compare them regarding their convergence, quality, and dispersion of solutions. The optimization target is the Gaussian Adaptive PID control (GAPID) to find the best parameters to achieve enhanced performance and robustness to load variations related to the traditional PID. The adaptive rule of GAPID is based on a Gaussian function that has as adjustment parameters its concavity and the lower and upper bound of the gains.

Optimization Tools Based on Metaheuristics for Performance Enhancement in a Gaussian Adaptive PID Controller.

This paper presents the proposal of using two bio-inspired metaheuristics-genetic algorithms (GAs) and particle swarm optimization (PSO)-to adjust the free coefficients of a Gaussian adaptive proportional-integral-derivative (GAPID) controller. When a specific adaptation rule is imposed to a conventional proportional-integral-derivative (PID) controller, either by means of a hyperbolic tangent function or a Gaussian function, the solution is left exposed to the function restrictions/impositions. Finding the correct proportionality between the parameters is an arduous task, which often does not have an algebraic solution.