Stability analysis of sampled-data systems via novel Lyapunov functional method

2021 ◽  
Author(s):  
Zhaoliang Sheng ◽  
Chong Lin ◽  
Bing Chen ◽  
Qing-Guo Wang
Keyword(s):  
Stability Analysis ◽  
Lyapunov Functional ◽  
Functional Method ◽  
Data Systems ◽  
Sampled Data ◽  
Lyapunov Functional Method

scholarly journals Control Method for Sampled-Data Systems with Multiple Channels Based on Deadband-Triggered Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ying-Ying Liu ◽  
Yun-kai Chu
Keyword(s):  
Control Method ◽  
Functional Method ◽  
Multiple Channels ◽  
Data Systems ◽  
Sampled Data ◽  
Present Value ◽  
Time Signals ◽  
Communication Constraint ◽  
Lyapunov Functional Method ◽  
The Difference

A new deadband-triggered scheme is proposed to investigate the control problems for sampled-data systems with multiple transmitting channels. Sampled-data systems simultaneously contain continuous-time and discrete-time signals, which make the systems hybrid. In the sampled-data systems with multiple channels, the every state signals are transmitting at different channels. The deadband communication constraint is adopted to reduce the usage of communication resources. When the difference between the previous value and the most present value is lager than a given threshold of deadband, then the node of channels transmits the most present value. Furthermore, by use of Lyapunov functional method and input delay approach, the new stability analysis and stabilization conditions for the sampled-data with multiple channels on the basis of deadband-triggered scheme are proposed. Numerical simulations and experiments show the validity and usefulness of the derived conditions. The proposed deadband-triggered scheme is beneficial to further reduce the load of the communication data.

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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