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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2022, Vol. 39 ›› Issue (4): 559-570.doi: 10.3969/j.issn.1005-3085.2022.04.005

• • 上一篇    下一篇

永磁同步电动机位置伺服系统的自适应神经网络控制

于  洋,   吴  峰,   王  巍   

  1. 辽宁工业大学电气工程学院，锦州 121001
  • 出版日期:2022-08-15 发布日期:2022-10-15
  • 基金资助:
    国家自然科学基金 (61603165)；辽宁省自然科学基金 (2019-BS-119).

Adaptive Neural Network Control of the Permanent Magnet Synchronous Motor Servo System

YU Yang,   WU Feng,   WANG Wei   

  1. School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001
  • Online:2022-08-15 Published:2022-10-15
  • Supported by:
    The National Natural Science Foundation of China (61603165); the Natural Science Foundation of Liaoning Province (2019-BS-119).

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摘要： 针对需要考虑参数不确定和负载扰动的永磁同步电动机位置伺服系统，提出了一种新型的自适应神经网络控制方法。首先，利用神经网络建立永磁同步电动机的智能模型。其次，针对模型特点，在反步递推设计框架下，应用神经网络基函数的本质特征，并引入动态面控制技术克服控制设计中存在的“复杂性爆炸”问题，设计基于自适应神经网络动态面控制的位置跟踪算法。最后，仿真结果表明该控制方案是有效可行的，与反步递推控制方案相比，基于神经网络动态面控制的位置伺服系统的跟踪误差具有更快的收敛速度。通过设计新的神经网络自适应律，提出的自适应神经网络控制方法可以避免现有反步递推控制设计中存在的代数环问题。此外，提出的控制算法不仅能够克服不确定性因素对系统性能的影响，而且算法结构简单，易于实现。

关键词: 永磁同步电动机, 神经网络控制, 动态面控制, 位置跟踪

Abstract:

A novel adaptive neural network control method is proposed for the permanent magnet synchronous motor position servo system considering parameter uncertainties and load torque disturbances. First, neural networks are utilized to construct the intelligent model of the permanent magnet synchronous motor. Then, on the basis of backstepping control design and applying the characteristic of neural network basis function, an adaptive neural network dynamic surface control algorithm for position tracking is designed, which can overcome the ``explosion of complexity" problem. Finally, simulation results are given to verify the effectiveness of the designed control scheme. Compared with the traditional backstepping control scheme, the position servo system based on the neural network dynamic surface control can converge faster. The algebraic loop problem is overcome by designing novel adaptive parameter laws of the neural network weights. In addition, the proposed control algorithm can overcome the influence of the uncertain factors on the system performance with simple structure and easy implementation.

Key words: permanent magnet synchronous motor, neural network control, dynamic surface control, position tracking

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