In this paper, a new adaptive super-twisting sliding mode control (STSMC) scheme based on a double loop fuzzy neural network (DLFNN) is proposed to solve the problem of the external disturbances and approximate the unknown model for a micro gyroscopes. The STSMC algorithm can effectively suppress chattering since it can hide the high-frequency switching part in the high-order derivative of the sliding mode variable and transfer the discrete control law to the high-order sliding mode surface. Because it not only combines the advantages of fuzzy systems, but also incorporates the advantages of neural network control, the proposed double loop fuzzy neural network can better approximate the system model with excellent approximation. Moreover, it has the advantage of full adjustment, and the initial values of all parameters in the network can be arbitrarily set, then the parameters can be adjusted to the optimal stable value adaptively according to the adaptive algorithm. Finally, the superiority of the STSMC algorithm is also discussed. Simulation results verify the superiority of the STSMC algorithm, showing it can improve system performance and estimate unknown models more accurately compared with conventional neural network sliding mode control (CNNSMC).

本文提出了一种基于双环模糊神经网络（DLFNN）的自适应超扭曲滑模控制（STSMC）方案，以解决外界干扰问题并近似微陀螺仪的未知模型。STSMC算法可以有效地抑制抖动，因为它可以将高频开关部分隐藏在滑模变量的高阶导数中，并将离散控制律传递到高阶滑模表面。由于它不仅结合了模糊系统的优点，而且还结合了神经网络控制的优点，因此所提出的双环模糊神经网络可以更好地近似系统模型，并且具有出色的近似性。而且，它具有充分调整的优势，可以任意设置网络中所有参数的初始值，然后根据自适应算法将参数自适应地调整为最佳稳定值。最后，还讨论了STSMC算法的优越性。仿真结果验证了STSMC算法的优越性，表明与传统的神经网络滑模控制（CNNSMC）相比，它可以提高系统性能并更准确地估计未知模型。