舵机永磁无刷直流电机控制器参数离线整定

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摘要针对电动舵机伺服系统控制参数较难整定问题, 提出一种基于神经网络和遗传算法极值寻优的PI控制器参数离线整定方法。采用电机舵机伺服系统的实验数据训练BP神经网络, 并选择预测精度最高的神经网络模型的输出作为遗传算法的优化目标函数的输入, 采用遗传算法快速搜索, 得到使舵机伺服系统的动态响应性能最好的PI控制器参数组合。并在基于无刷直流电机驱动的电动舵机平台上进行实验, 结果表明采用优化后的PI控制参数, 舵机系统能够快速、平稳而又准确地跟踪输入指令变化, 抗干扰能力强。

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	崔业兵
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关键词 ：遗传算法, 神经网络, 无刷直流电机, 电动舵机, 优化

Abstract：According to the difficulty of control parameters tuning for electromechanical actuator(EMA) servo system, a new method of off-line tuning proportional integral(PI) controller’s coefficients is proposed, which based on error back propagation(BP) Artificial neural network(ANN) and genetic algorithm(GA). BP Artificial neural network(BP-ANN) is trained using EMA servo system experimental data, and the highest prediction precision BP-ANN model’s outputs are chosen as optimal objective function of the genetic algorithm’s inputs, then optimal values of PI controller’s coefficients were obtained through using genetic algorithm to quick research, which ensuring the perfect dynamic response performance of the whole system. The MEA system driving by brushless DC motor(BLDC) was used to carry out speed control experiment. The results adopting the optimal PI controller’s coefficients show that the system’s output can follow the input instruction change rapidly, steadily and accurately, and it has strong anti-interference ability as well.

Key words： Genetic algorithm neural network brushless DC motor electromechanical actuator optimization

收稿日期: 2012-06-13 出版日期: 2014-03-25

PACS:

TM351

基金资助:江苏省研究生科研创新计划资助项目(CXZZ13_0219)

作者简介: 崔业兵男, 1983年生, 博士研究生, 主要研究方向为电动舵机控制, 电机控制与仿真方面的研究。郑健男, 1979年生, 博士, 讲师, 主要从事电动舵机控制, 飞行器总体与发动机技术方面的研究。

引用本文:

崔业兵, 郑健, 鞠玉涛, 赵金星. 舵机永磁无刷直流电机控制器参数离线整定[J]. 电工技术学报, 2013, 28(9): 281-287. Cui Yebing, Zheng Jian, Ju Yutao, Zhao Jinxing. Off-Line Controller Parameters Tuning for Electromechanical Actuator Systems with Permanent Magnet Brushless DC Motor Driving. Transactions of China Electrotechnical Society, 2013, 28(9): 281-287.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2013/V28/I9/281

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