Intelligent position control of permanent magnet synchronous motor using recurrent fuzzy neural cerebellar model articulation network

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Title:	Intelligent position control of permanent magnet synchronous motor using recurrent fuzzy neural cerebellar model articulation network
Authors:	林法正;Lin, Faa-Jeng;Yang, Kai-Jie;Sun, I-Fan;Chang, Jin-Kuan
Contributors:	資訊電機學院電機工程學系
Keywords:	32‐bit floating‐point DSP;Artificial neural networks;computed torque controller;Control systems;Controllers;digital signal processing chips;digital signal processor;DSP‐based recurrent fuzzy neural cerebellar model articulation network;fault tolerant control;fault‐tolerant control scheme;fuzzy control;generalisation performance;intelligent control;intelligent position control;localisation learning;machine control;Networks;neurocontrollers;online learning;periodical position references;permanent magnet motors;permanent magnet synchronous motor;Permanent magnets;PMSM position servo drive system;position control;position servo drive systems;RFNCMAN control;rotor position reference command;rotors;Servocontrol;Servomechanisms;six‐phase permanent magnet synchronous motor;synchronous motor drives;Synchronous motors;torque control
Date:	2015-01-01
Issue Date:	2026-04-23 14:18:27 (UTC+8)
Publisher:	Institution of Engineering and Technology;The Institution of Engineering and Technology
Abstract:	摘要： A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) control is proposed in this paper for position servo drive systems to track various periodical position references with robustness. The adopted position servo drive system is designed using a six-phase PMSM and equipped with a fault-tolerant control scheme. First, an ideal computed torque controller is designed for the tracking of the rotor position reference command. Since the uncertainties of the PMSM position servo drive system are difficult to know in advance, it is impossible to design an ideal computed control law for practical applications. Therefore, the RFNCMAN is proposed to mimic the ideal computed torque controller with a compensated controller to compensate the approximation error. In the RFNCMAN, a recurrent fuzzy cerebellar model articulation network (RFCMAN) is adopted in the first dimension to enhance the online learning rate and localisation learning capability. Moreover, a general recurrent fuzzy neural network (RFNN) is adopted in the second dimension to enhance the generalisation performance and to reduce the required memory and rule numbers. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP. The effectiveness of the proposed RFNCMAN control system is verified by some experimental results. 出版者： The Institution of Engineering and Technology 出版日期： 2015-03 出處： IET electric power applications, 2015-03, Vol.9 (3), p.248-264 資源來源： Wiley Online Library Open Access 版權： The Institution of Engineering and Technology 版權： 2020 The Institution of Engineering and Technology 識別號： ISSN: 1751-8660 識別號： ISSN: 1751-8679 識別號： EISSN: 1751-8679 識別號： DOI: 10.1049/iet-epa.2014.0088
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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