Abstract
In this paper, we propose an accelerated adaptive backstepping control algorithm based on the type2 sequential fuzzy neural network (T2SFNN) for the microelectromechanical system (MEMS) gyroscope with deadzone and constraints. Firstly, the mathematical model of the MEMS gyroscope is established to perform dynamical analyses and controller design. Then, the phase diagrams and Lyapunov exponents are presented to reveal its chaotic oscillation, which is harmful to system stability. In order to suppress oscillations derived from chaos and deadzone, an accelerated adaptive backstepping controller is proposed wherein an adaptive auxiliary is established to compensate the influence of nonsymmetric deadzone on stability performance, along with the T2SFNN designed to approximate unknown functions of dynamic systems. Furthermore, the speed function is introduced to accelerate convergence speed of the control system, and the problem of complex term explosion in traditional backstepping is successfully solved by a secondorder tracking differentiator. Finally, simulation results show that the proposed control scheme can guarantee asymptotic convergence of all signals in the closedloop system, as well as satisfying states constraints and fulfilling the purposes of chaos suppression and accelerated convergence.
Highlights
In view of the advantages of measuring the angular velocity of objects, low energy consumption, high integration, and simple structure, the MEMS gyroscope is widely used in vehicle navigation and positioning system, control, aerospace, the social robot, and other fields (Lin, Li, and Yang, 2020; Chong et al, 2016; Fang, Fei, and Yang, 2018; Rahmani, 2018; Rahmani and Rahman, 2018; Su, Li, and Yang, 2020)
Attribution 4.0 International (CC BY 4.0) Share - Adapt such as dead-zone hysteresis and chaotic oscillations, will reduce its operational performance, and even cause serious safety accidents. It is of profound and lasting significance to design an effective controller to improve the robust performance of the MEMS gyroscope and suppress the chaotic oscillations within it
In order to address the control problem of the MEMS resonator, Luo and Song (2016) proposed an adaptive backstepping control method based on RBF neural networks with output constraints and uncertain time delays
Summary
In view of the advantages of measuring the angular velocity of objects, low energy consumption, high integration, and simple structure, the MEMS gyroscope is widely used in vehicle navigation and positioning system, control, aerospace, the social robot, and other fields (Lin, Li, and Yang, 2020; Chong et al, 2016; Fang, Fei, and Yang, 2018; Rahmani, 2018; Rahmani and Rahman, 2018; Su, Li, and Yang, 2020). In order to address the control problem of the MEMS resonator, Luo and Song (2016) proposed an adaptive backstepping control method based on RBF neural networks with output constraints and uncertain time delays.
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