Application of artificial neural network for adaptive speed control of PMSM drive with variable parameters

Purpose - The aim of the research was to find out a method of adaptive speed control robust against variation of selected parameters of system like moment of inertia, time constant of torque control loop or torque coefficient of the motor. Design/methodology/approach - The main goal of the research was achieved due to application of artificial neural network (ANN), which was trained on line on the base of speed control error. The good results were gained by elaboration of enough fast and precise training algorithm and proper ANN structure. Findings - The work shows a structure of artificial neural network (ANN), applied as adaptive speed controller, and presents an algorithm of ANN training. Some versions of this algorithm were analysed and verified by simulation and experimental tests. Research limitations/implications - The research should be continued to determine a final version of training algorithm and its influence on controller properties. Practical implications - The elaborated adaptive controller can be easily used by applying microprocessor system available now on the market. The proposed control solution is robust against parameters variation as well as their imprecise identification. The controller has ability of self-tuning which can have great practical advantage. Social implications - Social implications are difficult to determine. Originality/value - The paper presents a new solution of adaptive speed controller, which means a new ANN structure and new training algorithm.