Optimal Fuzzy Logic Controller Using Teaching Learning Based Optimization for asynchronous motor

In this work, a nonlinear control algorithm, based on two Fuzzy Logic Controllers (FLCs) and a meta-heuristic optimizer, is proposed. This strategy aims to control the mechanical speed of a three-phase asynchronous motor. Indeed, the control signal is generated based on two factors namely frequency and magnitude, which are calculated by the two FLCs. To obtain good control performance, the parameters of the FLCs are suitably tuned and optimized using the Teaching Learning Based Optimization (TLBO) algorithm. The TLBO is a meta-heuristic algorithm that was implemented in many engineering application and gained wide acceptance among the optimization researchers community. Furthermore, except the common meta-heuristic parameters, the TLBO does not require any algorithm specific parameters. To assess the effectiveness of the proposed control algorithm, the control of a squirrel cage induction machine is considered. A comparative study with scalar control architecture using the Particle Swarm Optimization based PID controller, is carried out. The obtained results indicate that the proposed control algorithm gives better control performance than the other controllers.