具有状态约束和输入非线性的PMSLM自适应神经网络控制  

Adaptive Neural Network Control for Permanent Magnet Synchronous Linear Motor with State Constraints and Input Nonlinearities

作　　者：曹阳 郭健 CAO Yang;GUO Jian(School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China.)

机构地区：[1]南京理工大学自动化学院,江苏南京210094

出　　处：《东北大学学报（自然科学版）》2023年第11期1556-1563,共8页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(62303224);江苏省研究生科研与实践创新计划项目(KYCX19_0303)。

摘　　要：针对永磁同步直线电机系统中存在的模型不确定性、状态约束以及输入非线性(如非线性电磁驱动力/输入受限)问题,提出了一种基于神经网络的自适应控制器.具体来说,为了降低噪声敏感性,进一步提高跟踪精度,采用仅依赖于参考轨迹的期望信号来替代测量信号.然后,设计神经网络在线逼近未知模型和非线性函数,并通过构造连续控制的方法来处理逼近误差.此外,构造障碍李雅普诺夫函数确保系统在运行过程中状态始终满足约束条件;并且通过严格的理论分析证明了跟踪性能满足要求.最后,通过仿真实验验证了所提控制器的有效性和鲁棒性.An adaptive controller based on neural network is proposed for the problems of model uncertainty,state constraints and input nonlinearities(such as nonlinear electromagnetic drive force/input limitation)in permanent magnet synchronous linear motor(PMSLM).Specifically,in order to reduce the noise sensitivity and further improve the tracking accuracy,the desired signal that only depends on the reference trajectory is used to replace the measurement signal.Then,the neural network is designed to approach the unknown model and the nonlinear function online,and the approximation error is processed by constructing continuous control.In addition,an obstacle Lyapunov function is constructed to ensure that the state of the system always satisfies the constraints during the operation process;through strict theoretical analysis,it is proved that the tracking performance meets the requirements.Finally,simulation experiments verify the effectiveness and robustness of the proposed controller.

关 键 词：永磁同步直线电机 模型不确定性 状态约束 输入非线性 神经网络 

分 类 号：TM351[电气工程—电机]