The invention discloses an MEMS
gyroscope robust self-
adaptation control method based on neural network upper bound learning. The method includes the following steps that an ideal
kinetic model and an MEMS
gyroscope kinetic model are established, a sliding mode function is designed, a control law is obtained based on the sliding mode function, and an RBF neural network upper bound estimated value is used as a
gain of a robust item on the basis of the control law together with a feedback item and the robust item; a parameter self-
adaptation law and a network weight self-
adaptation law are designed based on a Lyapunov method. According to the MEMS
gyroscope robust self-adaptation control method based on neural network upper bound learning, the feedback item is added in the control law, the two-shaft vibration trajectory tracking speed and the parameter
estimation speed of an MEMS gyroscope are greatly increased, and the
vibration amplitude is decreased; the robust item based on RBF neural network upper bound learning is added in the control law, the buffeting problem caused by large external disturbance and fluctuation and the problem that the dynamic characteristics are changed worse are solved, the uncertainty of a
structural formula and the uncertainty of a non-structured formula are eliminated, and therefore the robustness of the
system is further improved.