Open AccessIntelligent tracking control of a PMLSM using self‐evolving probabilistic fuzzy neural network
Author(s) -
Chen SyuanYi,
Liu TungSheng
Publication year - 2017
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2016.0819
Subject(s) - control theory (sociology) , servomechanism , artificial neural network , robustness (evolution) , fuzzy logic , control engineering , computer science , fuzzy control system , engineering , artificial intelligence , control (management) , biochemistry , chemistry , gene
This study presents a self‐evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN‐AMF) controller for the position servo control of a permanent magnet linear synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field‐oriented control. Subsequently, an SPFNN‐AMF control system, which integrates the advantages of self‐evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time‐varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN‐AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN‐AMF for controlling the PMLSM servo drive system.