Practically Predefined-Time Adaptive Fuzzy Quantized Control for Nonlinear Stochastic Systems With Actuator Dead Zone
Tianliang Zhang, Rui Bai, Yongming Li
Abstract
This article focuses on the practically predefined-time adaptive fuzzy quantized control for nonlinear stochastic systems with actuator dead zone. Fuzzy logic systems are employed to approximate uncertain nonlinear functions. A novel stochastic predefined-time control scheme is proposed, which can help reduce the control parameters and increase the robustness of the closed-loop system. Taking the quantization and dead zone in the control link into account, the adaptive parameters and a part of the control are used to estimate and compensate the nonlinear disturbance, respectively. In addition, under reasonable assumptions, the complexity of the Lyapunov function compared with conventional stochastic adaptive control is reduced. Based on the stochastic predefined-time stabilization theory, an adaptive fuzzy controller is designed to make the upper bound of the expected settling time arbitrarily configured. Finally, two examples show the effectiveness of the main results.