基于微粒群算法的无刷直流电机单神经元自适应控制

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Abstract A single neuron adaptive speed control algorithm based on particle swarm optimization (PSO) is proposed in order to improve the speed control performance of brushless DC motor (BLDCM). The ability of the single neuron which can adjust conjunction weights on-line is used to make adaptive speed control. Aimed at the disadvantage that the weight-adjusting rule of the single neuron is prone to be trapped in local optimum, it was proposed to use PSO which has good ability to make both global and local optimization to adjust the weights of single neuron on-line. As a result, the algorithm improves the self-learning and adaptive ability of the single neuron. Matlab simulation and experimental results indicate that under the proposed algorithm the overshoot of the system is small and the speed response is fast with a little fluctuation. The proposed algorithm has better dynamic characteristic and robustness than traditional PID control.

Key words： Brushless DC motor (BLDCM) particle swarm optimization (PSO) single neuron adaptive control

Received: 10 December 2009 Published: 07 March 2014

PACS:

TM383

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	Articles by authors
	Dai Rui
	Cao Longhan
	He Junqiang
	Tang Chao
	Liu Xiaoli

Cite this article:

Dai Rui,Cao Longhan,He Junqiang等. Particle Swarm Optimization Based Single Neuron Adaptive Control for Brushless DC Motor[J]. Transactions of China Electrotechnical Society, 2011, 26(4): 57-63.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2011/V26/I4/57

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