A NEURO-GENETIC CONTROLLER FOR NONMINIMUM-PHASE SYSTEMS

This paper investigates a neuro-controller for nonminimum phase systems which is trained off-line with genetic algorithm (GA) and is combined in parallel with a conventional linear controller of proportional plus integral plus derivative (PID) type. Training of this kind of a neurogenetic controller provides a solution under a given global evaluation I function, which is devised based on the desired control performance during the whole training time interval. Empirical simulation results illustrate the efficacy of the proposed controller compared with a conventional linear controller in point of learning capability of adaptation and improvement of performances of a step response like fast settling time, small undershoot, and small overshoot.