Improving settling and rise times of controllers via intelligent algorithms

Generally, conventional controllers are characterized by too longs settling and rise times. In order to solve this problem, suitable fuzzy logic controllers have been designed. However, some intelligent techniques can be added during the controllers designing phase. In the literature, the employed methods are Genetic Algorithms and Neural Networks. The first ones are good search methods whereas the others ones have the capability to learn from data. In this paper, an optimized genetic-neuro-fuzzy controller is proposed. This controller works in according with a real-time optimization algorithm which optimally combines the features of Fuzzy Logic, Genetic Algorithms and Neural Networks. The genetic procedures search the optimal membership functions whereas the neural methods optimize the fuzzy rules. The target is to reduce the settling time and rise time with overshoot equal to zero. The novelty of this approach is that the optimization procedures occur at the same time and not separately. The results show that the settling time and the rise time are reduced by comparing them with the same quantities of optimized PD and PID controllers. Moreover, the designed controller improves the timing performance of conventional and intelligent controllers. © 2012 IEEE.