Sensorless induction spindle motor drive using fuzzy neural network speed controller |
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| Affiliation: | 1. Industrial Engineering Center, Zhejiang Province Key Laboratory of Advanced Manufacturing Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China;2. School of Aerospace, Transportation and Manufacturing, Cranfield University, Cranfield MK43 0AL, United Kingdom;3. Xi''an Research Institute of Navigation Technology, Xi''an 710068, Shaanxi, China;4. Shandong University of Science and Technology, Qingdao 266590, China;1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China;2. Centre for Precision Manufacturing, DMEM, University of Strathclyde, Glasgow G1 1XJ, UK;3. Machining and Condition Monitoring Group, Faculty of Engineering, University of Nottingham, NG7 2RD, UK;4. School of Intelligent Manufacturing Ecosystem, Xi''an Jiaotong-Liverpool University, Suzhou 215123, China;5. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China;1. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 55800, China;2. Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China;3. School of Machinery and Automation, Wuhan University of Science and Technology, Hubei 430081, China;4. MRC-University of Glasgow, University of Glasgow, Glasgow G12 8QB, UK;5. School of Management, China West Normal University, Nanchong 637002, China;1. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, 55800, China;2. Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan, 442002, China;3. School of Machinery and Automation, Wuhan University of Science and Technology, Hubei, 430081, China;4. MRC-University of Glasgow, University of Glasgow, Glasgow, G12 8QB, UK |
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| Abstract: | A sensorless induction spindle motor drive using synchronous PWM and dead-time compensator with fuzzy neural network (FNN) speed controller is proposed in this study for advanced spindle motor applications. First, the operating principles of a new type synchronous PWM technique are described in detail. Then, a speed observer based on the model reference adaptive system (MRAS) theory is adopted to estimate the rotor speed. To increase the accuracy of the estimated speed, the speed estimation algorithm is implemented using a digital signal processor. Moreover, since the control characteristics and motor parameters for high speed operated induction spindle motor drive are time-varying, an FNN speed controller is developed to reduce the influence of parameter uncertainties and external disturbances. In addition, the FNN is trained on-line using a delta adaptation law. Finally, the performance of the proposed sensorless induction spindle motor drive system is demonstrated using some simulation and experimental results. |
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