|
|
|
| Neural Network Inverse Control of Speed Variable System for BLDCM |
| Liu Guohai1, Jin Peng1, Wei Haifeng2 |
| 1. Jiangsu University Zhenjiang 212013 China 2. Key Laboratory of Modern Agricultural Equipment and Technology Ministry of Education and Jiangsu Province Jiangsu University Zhenjiang 212013 China |
|
|
|
|
Abstract Variation of parameters and nonlinear commutation process limit the speed control performance of brushless DC motor(BLDCM). A control approach based on neural network inverse system was developed for the brushless DC motor by analyzing the mathematical model in conduction region and commutation region. The inverse system of BLDCM consists of a static artificial neural network (ANN) and two integrators. By cascading the inverse system with BLDCM, the nonlinear system was transformed into pseudo-linear system and the whole system performance is improved. Simulation and experimental results show that the proposed scheme reduces overshoot, preserves fast speed response merit, and shows robust to load disturbance and motor parameters uncertainty. It effectively improves dynamic and static operation performance, and is a novel control method for BLDCM.
|
|
Received: 24 June 2009
Published: 04 March 2014
|
|
|
|
|
|
[1] 李钟明, 刘卫国. 稀土永磁电机[M]. 北京: 国防工业出版社, 1999.
[2] 叶长青, 尹华杰. 无刷直流电机速度的模糊控制方法[J]. 电气传动, 2006, 36(3): 3-7.
[3] Rubaai A, Ricketts D, Kankam M D. Laboratory implementation of a microprocessor-based fuzzy logic tracking controller for motion controls and drives[J]. IEEE Trans. on Industry Application, 2002, 38(2): 448-456.
[4] Kumar M, Singh B P. Fuzzy pre-compensated PI controller for PMBLDC motor drive[C]. International Conference on Power Electronics, Drives and Energy System, India, 2006.
[5] Hyeung Sik Choi, Yong Heon Park, Minho Lee. Global sliding-mode control improved design for a brushless DC motor[J]. IEEE Control System Magazine, 2001, 21(3): 27-35.
[6] Thirusakthimurugan P, Dananjayan P. A new generalized predictive controller for the speed control of PMBLDC motor[C]. Third International Conference on Information and Automation for Sustainability, Victoria, Australia, 2007.
[7] 刘国海, 刘平原, 等. 基于神经网络广义逆的两电机变频系统解耦控制的试验研究[J]. 中国电机工程学报, 2008, 28(36): 98-103.
[8] 戴先中, 刘国海, 张兴华. 恒压频比变频调速系统的神经网络逆控制[J]. 中国电机工程学报, 2005, 25(7): 109-114.
[9] 刘国海, 孙玉坤, 张浩, 等. 基于神经网络逆系统的磁悬浮开关磁阻电动机的解耦控制[J]. 电工技术学报, 2005, 20(9): 39-43.
[10] Dai Xianzhong, He Dan, Zhang Teng, et al. ANN generalized inversion for the linearization and coupling control of nonlinear systems[J]. IEE Proceeding Control Theory and Applications, 2003, 150(3): 267-277.
[11] 张晓峰, 胡庆波, 吕征宇. 基于Buck变换器的无刷直流电转矩脉动抑制方法[J]. 电工技术学报, 2005, 20(9): 73-76.
[12] 牛全民, 亓迎川. 基于状态观测器实现转速及负载转矩估计的直流调速系统[J]. 电子技术应用, 2000(5): 24-26. |
|
|
|
2010, Vol. 25 
