PID controller parameters optimization using gravitational search algorithm

Heuristic optimization algorithms are used widely in nonlinear optimization problems. In this thesis, Gravitational Search Algorithm (GSA), which is a heuristic optimization algorithm, is used to determine PID controller parameters for Direct Current (DC) motor control system. The heuristic algorithms (Touring Ant Colony Optimization (TACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DE)) were separately run 100 times at maximum generation number in Kesler et al. (2011). Obtained average PID controller parameters are evaluated and compared with GSA?s results. As a result, in many categories the performance of GSA is better than the performances of TACO and GA, but the results show that the proposed GSA?s performance is lower than the performances of the PSO and DE.Keywords DC Motor, Gravitational Search Algorithm, Heuristic Algorithms, Optimization, PID