Robust adaptive nonsingular terminal sliding mode control of MEMS gyroscope using fuzzy-neural-network compensator

To attenuate the effect of time-varying parameters, quadrature errors, and external disturbances and realize finite-time control, a robust adaptive nonsingular terminal sliding mode (NTSM) tracking control scheme using fuzzy-neural-network (FNN) compensator is presented for micro-electro-mechanical systems (MEMS) vibratory gyroscopes in this paper. By introducing a nonsingular terminal sliding mode manifold, a novel terminal sliding mode controller is designed for MEMS gyroscopes, while ensuring the control system could reach the sliding surface and converge to equilibrium point in a finite period of time from any initial state. In the presence of unknown model uncertainties and external disturbances, an adaptive fuzzy-neural-network controller is employed to compensate such system nonlinearities and improve the tracking performance. Online fuzzy-neural-network weight tuning algorithms are derived in the sense of Lyapunov stability theorem to guarantee the network convergence as well as stable control performance. Numerical simulations for a MEMS gyroscope are provided to justify the claims of the proposed adaptive fuzzy-neural-network control scheme and demonstrate the satisfactory tracking performance and robustness.