Adaptive robust finite-time neural control of uncertain PMSM servo system with nonlinear dead zone
Adaptive robust finite-time neural control of uncertain PMSM servo system with nonlinear dead zone
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DOI：
10.1007/s00521-016-2260-5
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发表时间：
2016-03
影响因子：
6
通讯作者：
Qiang Chen;X. Ren;J. Na;Dong-dong Zheng
中科院分区：
文献类型：
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作者：
Qiang Chen;X. Ren;J. Na;Dong-dong Zheng
针对具有非线性死区输入的不确定永磁同步电机伺服系统，提出了一种自适应鲁棒有限时间神经网络控制方案。根据微分中值定理，将死区表示为线性时变系统，并利用简单的神经网络逼近包含死区的模型不确定性。然后，基于快速终端滑模控制原理设计了自适应有限时间控制器，并通过修改终端滑模流形来规避初始台积电的奇异性问题。通过对比实验验证了该方法的有效性和优越的性能。
In this paper, an adaptive robust finite-time neural control scheme is proposed for uncertain permanent magnet synchronous motor servo system with nonlinear dead-zone input. According to the differential mean value theorem, the dead zone is represented as a linear time-varying system, and the model uncertainty including the dead zone is approximated by using a simple neural network. Then, an adaptive finite-time controller is designed based on a fast terminal sliding mode control principle, and the singularity problem in the initial TSMC is circumvented by modifying the terminal sliding manifold. Comparative experiments are conducted to validate the effectiveness and superior performance of the proposed method.