基于模糊和神经网络的微陀螺鲁棒自适应控制研究

MEMS(Micro-Electro-Mechanical Systems) triaxial gyroscope is a basic measurement component of inertial navigation and guidance system, manufacturing errors, parameter uncertainties and external disturbances will degrade the sensitivity and accuracy of the system. In this project, we will systematically and deeply study the robust intelligent adaptive control theory and method for the MEMS gyroscope, comprehensively use adaptive control, backstepping control, fuzzy control and neural network control method to online identify and compensate for the parameters of the MEMS gyroscope, and establish a theoretical framework for robust intelligent adaptive control theory of the MEMS gyroscope. We will study robust adaptive control problem of the MEMS gyroscope based on fuzzy and neural network algorithm；and will then study intelligent robust adaptive fuzzy neural controller for the MEMS gyroscope；and will also study adaptive fuzzy backstepping and adaptive neural backstepping controller for the MEMS gyroscope. The objective of this research is to identify system parameters, online estimate and compensate manufacturing errors, parameter uncertainties and external disturbances, and to improve the robustness, sensitivity and accuracy of the control system, and also to improve the performance of the MEMS gyroscope. The research of this project has fundamental significance for the MEMS gyroscope and intelligent adaptive control technology in the field of engineering and technology, and the research results have important academic value and application potential.

三轴微陀螺仪是惯性导航和惯性制导系统的基本测量元件,制造误差、参数不确定性和外界干扰都会降低系统的灵敏度和精度。本项目将系统深入研究三轴微陀螺仪鲁棒智能自适应控制理论和方法，综合运用自适应控制、反演控制、模糊控制和神经网络控制等方法对微陀螺仪进行在线参数估计和补偿, 建立微陀螺鲁棒智能自适应控制理论框架。研究基于模糊和神经网络算法的微陀螺仪鲁棒智能自适应控制问题；研究微陀螺仪模糊神经网络自适应控制理论与方法；研究微陀螺模糊自适应反演控制和神经网络反演自适应控制理论与方法。研究目的是辨识系统参数,在线估计和补偿制造误差、参数不确定性和外界干扰, 提高控制系统的鲁棒性、灵敏度和精度，并改善微陀螺仪的性能。本项目的研究对工程技术领域中的三轴微陀螺仪及智能自适应控制技术有基础性的指导意义,研究成果具有重要的学术价值和应用前景。

三轴微陀螺是惯性导航和惯性制导系统的基本测量元件,制造误差、参数不确定性和外界干扰都会降低系统的灵敏度和精度。本项目将深入研究三轴微陀螺鲁棒智能自适应控制理论和方法，综合运用自适应控制、反演控制、模糊控制和神经网络控制等方法对微陀螺进行参数估计和补偿, 研究基于模糊和神经网络算法的微陀螺鲁棒智能自适应控制；微陀螺模糊神经网络自适应控制；微陀螺模糊自适应反演控制和神经网络反演自适应控制。本项目的,研究成果具有重要的学术价值和应用前景。