Distributed Adaptive Controller for Output-Synchronization of Networked Strict Feedback Systems with Dead-Zone Nonlinearity with Application in the Voltage Equalization Control of Ultra-Capacitor Type Power Source

2021 ◽  
Author(s):  
Liming Zhang ◽  
Miao Yu
Keyword(s):  
Dead Zone ◽  
Adaptive Controller ◽  
Power Source ◽  
Feedback Systems ◽  
Output Synchronization ◽  
Type Power ◽  
Ultra Capacitor ◽  
Strict Feedback

scholarly journals Event-Triggered Adaptive Backstepping Control for Strict-Feedback Nonlinear Systems with Zero Dynamics

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Bo Xu ◽  
Xiaoping Liu ◽  
Huanqing Wang ◽  
Yucheng Zhou
Keyword(s):  
Measurement Errors ◽  
Closed Loop ◽  
Control Strategies ◽  
Design Procedure ◽  
Adaptive Controller ◽  
Zero Dynamics ◽  
Feedback Systems ◽  
Adaptive Backstepping ◽  
Event Triggered ◽  
Strict Feedback

This paper focuses on the problem of event-triggered control for a class of uncertain nonlinear strict-feedback systems with zero dynamics via backstepping technique. In the design procedure, the adaptive controller and the triggering event are designed at the same time to remove the assumption of the input-to-state stability with respect to the measurement errors. Besides, we propose an assumption to deal with the problem of zero dynamics. Three different event-triggered control strategies are designed, which guarantees that all the closed-loop signals are globally bounded. The effectiveness of the proposed methods is illustrated and compared using simulation examples.

scholarly journals Constrained Adaptive Neural Control of Nonlinear Strict-Feedback Systems with Input Dead-Zone

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jingping Shi ◽  
Zhonghua Wu ◽  
Jingchao Lu
Keyword(s):  
Neural Network ◽  
Neural Control ◽  
Dead Zone ◽  
Tracking Error ◽  
State Feedback Control ◽  
System Transformation ◽  
Feedback Systems ◽  
Limit Condition ◽  
Performance Function ◽  
Strict Feedback

This paper focuses on a single neural network tracking control for a class of nonlinear strict-feedback systems with input dead-zone and time-varying output constraint via prescribed performance method. To release the limit condition on previous performance function that the initial tracking error needs to be known, a new modified performance function is first constructed. Further, to reduce the computational burden of traditional neural back-stepping control approaches which require all the virtual controllers to be necessarily carried out in each step, the nonlinear items are transmitted to the last step such that only one neural network is required in this design. By regarding the input-coefficients of the dead-zone slopes as a system uncertainty and introducing a new concise system transformation technique, a composite adaptive neural state-feedback control approach is developed. The most prominent feature of this scheme is that it not only owes low-computational property but also releases the previous limitations on performance function and is capable of guaranteeing the output confined within the new form of prescribed bound. Moreover, the closed-loop stability is proved using Lyapunov function. Comparative simulation is induced to verify the effectiveness.

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