Accelerated Adaptive Backstepping Control of the Chaotic MEMS Gyroscope by Using the Type-2 Sequential FNN

In this paper, we propose an accelerated adaptive backstepping control algorithm based on the type2 sequential fuzzy neural network (T2SFNN) for microelectromechanical system (MEMS) gyroscope with deadzone and constraints. Firstly, mathematical model of MEMS is established to perform dynamical analyses controller design. Then, phase diagrams Lyapunov exponents are presented reveal its chaotic oscillation, which harmful stability. order suppress oscillations derived from chaos deadzone, proposed wherein auxiliary compensate influence nonsymmetric stability performance, along T2SFNN designed approximate unknown functions dynamic systems. Furthermore, speed function introduced accelerate convergence system, problem complex term explosion in traditional successfully solved by a secondorder tracking differentiator. Finally, simulation results show that scheme can guarantee asymptotic all signals closedloop as well satisfying states constraints fulfilling purposes suppression convergence.