Self-Tuning Robust Stability Fuzzy Digital Controller

A self-tuning fuzzy control methodology via particle swarm optimization based on robust stability criterion, is proposed. The plant to be controlled is modeled considering a Takagi-Sugeno (TS) fuzzy structure from input-output experimental data, by using the fuzzy C-Means clustering algorithm (antecedent parameters estimation) and weighted recursive least squares (WRLS) algorithm (consequent parameters estimation), respectively. An adaptation mechanism based on particle swarm optimization is used to tune recursively the parameters of a model based fuzzy PID controller, from the gain and phase margins specifications. Computational results for adaptive fuzzy control of a thermal plant with time varying delay is presented to illustrate the efficiency and applicability of the proposed methodology.