A multiobjective evolutionary neuro-controller for nonminimum phase systems
Nonminimum phase systems are difficult to be controlled with a conventional PID-type controller because of their inherent characteristics of undershooting. A neuro-controller combined with a PID-type controller has been shown to improve the control performance of the nonminimum phase systems while maintaining stability. In this paper, we apply a multiobjective evolutionary optimization method for training the neurocontroller to reduce the undershooting of the nonminimum phase system. The computer simulation shows that the proposed multiobjective approach is very effective and suitable because it can minimize the control error as well as reduce undershooting and chattering. This method can be applied to many industrial nonminimum phase problems with ease.
neuro-control nonminimum phase systems multiobjective evolutionary algorithms neural networks