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首页> 外文期刊>Electric Power Applications, IET >Intelligent second-order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator
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Intelligent second-order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

机译：具有鲁棒补偿器的永磁线性同步电动机伺服系统智能二阶滑动模式控制

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摘要

In this study, an intelligent second-order sliding mode control (SMC) method combining second-order SMC (SOSMC) and recurrent radial basis function neural network (RRBFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is proposed to achieve high-performance servo control fields. On the basis of a dynamic model of PMLSM and the SMC theory, the chattering problem in SMC is weakened and the tracking accuracy is improved by the design of SOSMC. As for the boundary of the uncertainty factors is difficult to obtain, the optimal performance of SOSMC is hard to achieve, the RRBFNN uncertainty observer is introduced for estimating the value of the uncertainty factors. Owing to the strong learning ability, the network parameters can be trained online. Besides, a robust compensator is developed to suppress the uncertainties such as approximation error, optimal parameter vector and higher Taylor series for further improving the robustness. Moreover, the adaptive learning algorithms are obtained by using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments demonstrate that the proposed scheme provides high performance dynamic characteristics and strong robustness to uncertainties.
机译：在本研究中，提出了一种智能的二阶滑动模式控制（SMC）方法，其组合了适用于永磁线性同步电动机（PMLSM）的二阶SMC（SOSMC）和经常性径向基函数神经网络（RRBFNN）以实现高电平 - 格式伺服控制字段。在PMLSM的动态模型和SMC理论的基础上，SMC中的抖动问题被削弱，并且SOSMC的设计改善了跟踪精度。对于不确定性因素的边界难以获得，SOSMC的最佳性能难以实现，介绍了RRBFNN不确定性观察者，用于估计不确定性因素的价值。由于较强的学习能力，网络参数可以在线培训。此外，开发了一种强大的补偿器来抑制诸如近似误差，最佳参数向量和更高泰勒序列的不确定性，以进一步提高鲁棒性。此外，通过使用Lyapunov定理来保证系统的渐近稳定性来获得自适应学习算法。实验表明，该方案为不确定性提供了高性能的动态特征和强大的鲁棒性。

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