Adaptive control of MEMS gyroscope using global fast terminal sliding mode control and fuzzy-neural-network
Adaptive control of MEMS gyroscope using global fast terminal sliding mode control and fuzzy-neural-network
复制标题
使用全局快速终端滑模控制和模糊神经网络的 MEMS 陀螺仪自适应控制
DOI：
10.1007/s11071-014-1424-z
复制
发表时间：
2014-10-01
影响因子：
5.6
通讯作者：
Yan, Weifeng
中科院分区：
文献类型：
--
作者：
Fei, Juntao;Yan, Weifeng
本文针对微机电系统 (MEMS) 振动陀螺仪提出了一种使用全局快速终端滑模控制 (GTSMC) 和模糊神经网络 (FNN) 的 MEMS 陀螺仪自适应控制方法。该方法给出了一个新的全局快速终端滑模面，这将保证所设计的控制系统能够在更短的有限时间内从任何初始状态到达滑模面并收敛到平衡点。此外，所提出的自适应全局快速终端滑模控制器可以实时估计角速度以及阻尼和刚度系数。此外，该方案的主要特点是采用自适应模糊神经网络来学习模型不确定性和外部干扰的上限，因此不需要系统不确定性上限的先验知识。控制系统中的所有自适应律均在同一Lyapunov框架下推导，可以保证闭环系统的全局渐近稳定性。研究了 MEMS 陀螺仪的数值模拟，以证明所提出的控制方法的有效性。
An adaptive control of MEMS gyroscope using global fast terminal sliding mode control (GTSMC) and fuzzy-neural-network (FNN) is presented for micro-electro-mechanical systems (MEMS) vibratory gyroscopes in this paper. This approach gives a new global fast terminal sliding surface, which will guarantee that the designed control system can reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state. In addition, the proposed adaptive global fast terminal sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. Moreover, the main feature of this scheme is that an adaptive fuzzy-neural-network is employed to learn the upper bound of model uncertainties and external disturbances, so the prior knowledge of the upper bound of the system uncertainties is not required. All adaptive laws in the control system are derived in the same Lyapunov framework, which can guarantee the globally asymptotical stability of the closed-loop system. Numerical simulations for a MEMS gyroscope are investigated to demonstrate the validity of the proposed control approaches.