Self-Evolving Hermite Fuzzy Neural Fractional-Order Sliding Mode Control of MEMS Gyroscope

In attempt to ensure that the proof mass maintains the desired vibration modes, a fractional order sliding mode control (FOSMC) for MEMS gyroscopes based on a self-evolving Hermite fuzzy neural network (SEHFNN) has been proposed, where the FOSMC is crucial in the controller design to guarantee the tracking performance and a Hermite fuzzy neural network with a structural self-evolutionary mechanism is engaged in the controller implementation. The SEHFNN combines the advantages of both self-evolving fuzzy neural network (SEFNN) and Hermite neural network (HNN) to compensate for the unknown model parameters. The SEFNN is adapted to the current application scenario by a real-time structural adjustment mechanism, performed by the lightweight computation. The Hermite polynomial function used in HNN is able to take a full range of inputs without restriction and its role as a basis function can improve the generalization neural network ability. The performance effect is measured by calculating the RMSE parameter of the tracking error. Simulation experiments verified the robust performance of the proposed controller, showing it has higher control accuracy and smoother control input, indicating the proposed self-evolutionary mechanism completes the optimal structure adjustment successfully. Note to Practitioners—This paper was motivated by the problem of advanced control of MEMS gyroscopes. a fractional order sliding mode control using a self-evolving Hermite fuzzy neural network is proposed in this paper to maintain the trajectory tracking of proof mass. A Hermite fuzzy neural network with a structural self-evolutionary mechanism is introduced to be engaged in the implementation of the controller. The introduction of Hermite polynomial increases the depth of the network while improving the generalization ability of SEHFNN by decomposing the signal. Simulation studies prove the proposed control scheme has superior performance.