Sampled-data based asynchronous stabilization for switched systems with stochastic perturbations

2019 ◽  
Vol 42 (3) ◽  
pp. 439-450 ◽  
Author(s):  
Jianrong Zhao ◽  
Wen Wang ◽  
Dan Zhang

This paper studies the sampled-data based asynchronous control problem for switched nonlinear systems subject to stochastic perturbations. Applying the T-S fuzzy model, the sampled-data based asynchronous stabilization is studied for switched nonlinear systems subject to stochastic perturbations. Combining the sampled-data dependent Lyapunov functional with the mode-dependent average dwell-time technique, a fuzzy controller is obtained to stabilize switched nonlinear systems in the mean-square sense. No more than one switching and multiple switchings are both discussed in one sampling interval to achieve more common results. At last, a simulation example about nonlinear mass-spring mechanical systems subject to stochastic perturbations is given to illustrate the effectiveness of proposed results.

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.


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