ADAPTIVE DEADBEAT CONTROLLERS FOR BRUSHLESS DC DRIVES USING PSO AND ANFIS TECHNIQUES

The paper presents a tuning methodology for the parameters of adaptive current and speed controllers in a permanent-magnet brushless DC (BLDC) motor drive system. The parameters of both inner-loop and outer-loop PI controllers, which vary with the operating conditions of the system, are adapted in order to maintain deadbeat response for current and speed. Evenly distributed operating points are selected within preset regions of system loading. A particle swarm optimization (PSO) algorithm is employed in order to obtain the controller parameters assuring deadbeat response at each selected load. The resulting data from PSO are used to train adaptive neuro-fuzzy inference systems (ANFIS) that could deduce the controller parameters at any other loading condition within the same region of operation. The ANFIS agents are tested at numerous operating conditions indicating deadbeat response at all cases. The response of the developed controllers is compared to that of classical controllers whose parameters are tuned using the well-known Ziegler-Nichols method. Results signify the superiority of the proposed technique over the classical method.