Sampled-data based quantisation control for T–S fuzzy switched systems with actuator failures dependent on an improved Lyapunov functional method

2018 ◽  
Vol 12 (17) ◽  
pp. 2368-2379 ◽  
Author(s):  
Jianrong Zhao ◽  
Wei Liu ◽  
Guangming Zhuang ◽  
Yuming Chu ◽  
Yongmin Li
Keyword(s):  
Switched Systems ◽  
Lyapunov Functional ◽  
Functional Method ◽  
Sampled Data ◽  
Actuator Failures ◽  
Lyapunov Functional Method

scholarly journals A Novel Stability Analysis of Uncertain Switched Systems with Time-Varying Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ganji Huang ◽  
Shixian Luo ◽  
Linna Wei ◽  
Wuhua Chen
Keyword(s):  
Switched Systems ◽  
Lyapunov Functional ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Time Varying ◽  
System Parameters ◽  
Time Varying Delay ◽  
Lyapunov Functional Method ◽  
The Stability ◽  
Varying Delay

This paper deals with the stability of switched systems with time-varying delay. The time-varying system parameters are assumed to be norm-bounded. Based on a novel switched time-varying Lyapunov functional method, some new LMI-based sufficient conditions have been obtained to ensure the exponential stability for the uncertain switched delays systems. Finally, the proposed method is applied to a numerical example and the simulative results are also given.

scholarly journals Global Exponential Stability of Learning-Based Fuzzy Networks on Time Scales

2015 ◽  
Vol 2015 ◽  
pp. 1-15
Author(s):  
Juan Chen ◽  
Zhenkun Huang ◽  
Jinxiang Cai
Keyword(s):  
Exponential Stability ◽  
Time Scales ◽  
Continuous Time ◽  
Lyapunov Functional ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Fuzzy Neural Networks ◽  
New Learning ◽  
Fuzzy Neural ◽  
Lyapunov Functional Method

We investigate a class of fuzzy neural networks with Hebbian-type unsupervised learning on time scales. By using Lyapunov functional method, some new sufficient conditions are derived to ensure learning dynamics and exponential stability of fuzzy networks on time scales. Our results are general and can include continuous-time learning-based fuzzy networks and corresponding discrete-time analogues. Moreover, our results reveal some new learning behavior of fuzzy synapses on time scales which are seldom discussed in the literature.

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