Adaptive interval type-2 fuzzy recurrent RBFNN control design using ellipsoidal membership functions with application to MEMS gyroscope

Abstract

In this article, a hybrid control approach is provided to control the micro-electro-mechanical system (MEMS) triaxial gyroscope as a multi-input multi-output (MIMO) system. Control design includes a fast non-singular terminal sliding mode control (FNTSMC) as a main part of the proposed hybrid control method and since the MEMS gyroscope performance is affected by parameter variations, quadrature errors, and external disturbances in the core of the main controller, adaptive interval type-2 recurrent fuzzy radial basis function neural network (IT2-RFRBF-NN) is employed to estimate the lumped uncertainties. The proposed hybrid approach has four main attributes: (1) it lies in the category of model-free control structures; (2) There is no negative power involved. Hence, the suggested method does not have the singularity problem; (3) to enhance the capability of the proposed method in the present of noise the ellipsoidal membership functions are employed to design adaptive IT2-RFRBF-NN; (4) the Fourier series expansion as a function approximation technique is efficiently used to online estimate the discontinuous component by establishing a soft switching in the proposed controller. With the help of the Lyapunov stability theory, guaranteeing the closed-loop control system stability by the suggested control design is confirmed. The findings of the simulations and comparison with other approaches confirm the superiority of the suggested hybrid approach.