Abstract In this paper, an observer-based adaptive neural network finite-time dynamic surface control method is proposed for the position tracking control of PMSM stochastic nonlinear systems with iron losses. First, the finite-time technology is used to realize the fast and effective tracking of the desired signal and make the system have better robust performance. Then, the adaptive neural network (NN) technology and state observer are applied to approximating the uncertain nonlinear functions and estimating the immeasurable states, respectively. And, the dynamic surface control (DSC) technology is used to resolve the “explosion of complexity” problem. In addition, the influence of iron losses and stochastic disturbances in the system is considered, and a quartic stochastic Lyapunov function is established to analyze the stability of the system. Finally, the simulation results show the effectiveness of the proposed method.

中文翻译：

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基于动态表面技术的具有铁损的永磁同步电机随机非线性系统的神经自适应有限时间输出反馈控制

摘要 本文针对含铁损永磁同步电机随机非线性系统的位置跟踪控制，提出了一种基于观测器的自适应神经网络有限时间动态曲面控制方法。首先，利用有限时间技术实现对有用信号的快速有效跟踪，使系统具有更好的鲁棒性。然后，将自适应神经网络（NN）技术和状态观测器分别应用于不确定非线性函数的逼近和不可测状态的估计。并且，动态表面控制（DSC）技术用于解决“复杂性爆炸”问题。此外，还考虑了系统中铁损和随机扰动的影响，建立了四次随机Lyapunov函数来分析系统的稳定性。