Neural network-based adaptive fault tolerant consensus control for a class of high order multiagent systems with input quantization and time-varying parameters

2017 ◽  
Vol 266 ◽  
pp. 315-324 ◽  
Author(s):  
Zheng Wang ◽  
Jianping Yuan ◽  
Yanpeng Pan ◽  
Jinyuan Wei
Keyword(s):  
Neural Network ◽  
Multiagent Systems ◽  
Fault Tolerant ◽  
High Order ◽  
Time Varying ◽  
Consensus Control ◽  
Varying Parameters ◽  
Input Quantization ◽  
Time Varying Parameters

Identification of nonlinear systems with time-varying parameters using a sliding-neural network observer

2009 ◽  
Vol 72 (7-9) ◽  
pp. 1611-1620 ◽  
Author(s):  
Tarek Ahmed-Ali ◽  
Godpromesse Kenné ◽  
Françoise Lamnabhi-Lagarrigue
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Time Varying ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Neural Network Observer

scholarly journals Consensus Control of Time-Varying Delayed Multiagent Systems with High-Order Iterative Learning Control

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiongfeng Deng ◽  
Xiuxia Sun ◽  
Ri Liu ◽  
Shuguang Liu
Keyword(s):  
Theoretical Analysis ◽  
Multiagent Systems ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
High Order ◽  
Time Varying ◽  
Tracking Errors ◽  
Consensus Control ◽  
Control Protocol

We address the consensus control problem of time-varying delayed multiagent systems with directed communication topology. The model of each agent includes time-varying nonlinear dynamic and external disturbance, where the time-varying nonlinear term satisfies the global Lipschitz condition and the disturbance term satisfies norm-bounded condition. An improved control protocol, that is, a high-order iterative learning control scheme, is applied to cope with consensus tracking problem, where the desired trajectory is generated by a virtual leader agent. Through theoretical analysis, the improved control protocol guarantees that the tracking errors converge asymptotically to a sufficiently small interval under the given convergence conditions. Furthermore, the bounds of initial state difference and disturbances tend to zero; the bound of tracking errors also tends to zero. In the end, some cases are provided to illustrate the effectiveness of the theoretical analysis.

Synchronization of Chaotic Systems with Uncertain Time-Varying Parameters

2015 ◽  
Vol 9 (6) ◽  
pp. 568
Author(s):  
Ahmad Al-Jarrah ◽  
Mohammad Ababneh ◽  
Suleiman Bani Hani ◽  
Khalid Al-Widyan
Keyword(s):  
Chaotic Systems ◽  
Time Varying ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Uncertain Time
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