Exponential stability of feedback systems in differential form

2006 ◽  
pp. 58-100
Keyword(s):  
Exponential Stability ◽  
Differential Form ◽  
Feedback Systems

scholarly journals pth Moment Exponential Stability of Stochastic PWM Feedback Systems with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Zhong Zhang ◽  
Lixia Ye
Keyword(s):  
Exponential Stability ◽  
Pulse Width ◽  
Time Varying ◽  
Stability Criteria ◽  
Feedback Systems ◽  
Pulse Width Modulated ◽  
Globally Exponential Stability ◽  
Pth Moment Exponential Stability ◽  
Moment Exponential Stability ◽  
Theoretical Results

This paper further studies thepth moment exponential stability of stochastic pulse-width-modulated (PWM) feedback systems with distributed time-varying delays. We establish several globally exponential stability criteria for such PWM feedback systems by using Lyapunov-Krasovskii functional and then present an upper bound of the parameter of PWM when the system is stable and such system has stronger anti-interference performance than the system without time-varying delays. Furthermore, we present two examples to show the effectiveness and conservativeness of the theoretical results.

Discrete Feedback Systems 1.

2019 ◽  
Author(s):  
Péter Gáspár ◽  
Zoltán Szabó ◽  
József Bokor
Keyword(s):  
Feedback Systems

Optimal Model Reference Control Scheme Design for Nonlinear Strict-Feedback Systems

2020 ◽  
Vol 38 (9A) ◽  
pp. 1342-1351
Author(s):  
Musadaq A. Hadi ◽  
Hazem I. Ali
Keyword(s):  
Reference Model ◽  
Feedback System ◽  
Optimization Method ◽  
Feedback Systems ◽  
Model Reference ◽  
Scheme Design ◽  
Model Reference Control ◽  
Control Scheme ◽  
Lyapunov Stability Analysis ◽  
Strict Feedback

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.

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