An improved fuzzy learning algorithm for motion control applications [PM synchronous motors]

In this paper, the authors describe an improved fuzzy adaptation method to construct or change the knowledge base in the fuzzy logic controller (FLC). The objective of the fuzzy logic adaptation mechanism (FLAM) is to change the rules definition in the FLC rule base table, according to the comparison between a reference model output signal and the system output. The FLAM is composed by a fuzzy inverse model and a knowledge base modifier. The learning algorithm has a local effect but differently from previous fuzzy strategies it uses a weighting factor for each active rule, to avoid unnecessary control signal switching. They show the efficiency of this method in a TMS320C30 DSP-based speed fuzzy control scheme of a permanent magnet synchronous motor (PMSM). The fuzzy logic adaptive strategy can be easily implemented. It has fast learning features and very good tracking characteristics even under severe variations of the system parameters, due to the improved algorithm