Controller design for dc motor drives using multi-objective optimization evolutionary algorithms

Effective motor speed control for electric machine drives need to find a tradeoff among various conflicting control objectives. However, this task Is often not sufficiently cost-effective as accurate system modeling by taking all the nonlinear factors into account is very complex. As a result, the designed controller designer is not able to lead to optimum system performance. However, by using evolutionary computation based stochastic search techniques such as genetic algorithms (GAs) we can find a set of Pareto-optimal solutions. In this paper, a control scheme based on multi-objective optimization evolutionary algorithms (MOEAs) is proposed, which is able to tune the current controller and speed controller simultaneously in order to find Pareto-set optimization solution for a cascaded dc motor drive system.