TY - JOUR
T1 - Intelligent tracking control of a PMLSM using self-evolving probabilistic fuzzy neural network
AU - Chen, Syuan Yi
AU - Liu, Tung Sheng
N1 - Funding Information:
The authors acknowledge the financial supports of the National Taiwan Normal University (NTNU) through its grant T10407000655 and the Ministry of Science and Technology in Taiwan, R.O.C. under grant no. MOST 105-2221-E-003-014.
Publisher Copyright:
© The Institution of Engineering and Technology 2016.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
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U2 - 10.1049/iet-epa.2016.0819
DO - 10.1049/iet-epa.2016.0819
M3 - Article
AN - SCOPUS:85021894838
SN - 1751-8660
VL - 11
SP - 1043
EP - 1054
JO - IET Electric Power Applications
JF - IET Electric Power Applications
IS - 6
ER -