GA tuning of Fuzzy Controller for MIMO system

Genetic algorithms have demonstrated considerable success in providing good solutions to many hard optimization problems. For such problems, exact algorithms that always find an optimal solution are only useful for small optimization problems, so heuristic algorithms such as the genetic algorithm must be used in practice. In this paper, we apply the genetic algorithm to the nonlinear MIMO problem of complex objective function. We compare the genetic algorithm with the exact optimization results. Our empirical results indicate that by using the genetic algorithm is able to find an optimal solution at speed orders of magnitude faster than exact algorithms. Simulation results of a two-link robot arm are reported with different objective functions to confirm the validity of our assumption.