Fuzzy PID controller multiobjective genetic design

A robust fuzzy control design, with time delay, based on gain and phase margins specifications for nonlinear systems, in the continuous time delay, is proposed. From input and output data of the process, a Fuzzy C-Means (FCM) clustering algorithm estimates the antecedent parameters and the rules number of a Takagi-Sugeno fuzzy model, whereas the least squares algorithm estimates the consequent parameters. A multiobjective genetic strategy is developed to tune the fuzzy digital controller parameters, so the gain and phase specified margins are obtained for the fuzzy control system. The fuzzy PID controller was implemented on a real time acquisition data platform, based on CompactRIO (NI cRIO-9073) and LabVIEW, from National Instruments, for temperature control of a thermic process. The experimental results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the PID control system compared to the specified ones and tracking of the reference trajectory. Fuzzy PID controller also is more efficient when compared to the fuzzy delay and lead compensator.