scholarly journals Adaptive Dynamic Surface Decentralized Output Feedback Control for Switched Nonstrict Feedback Large-Scale Systems with Unknown Dead Zones

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Min Wan ◽  
Shanshan Huang
Keyword(s):  
Output Feedback ◽  
Large Scale ◽  
Dead Zone ◽  
Output Feedback Control ◽  
Dynamic Surface Control ◽  
Learning Technology ◽  
Dynamic Surface ◽  
Large Scale Systems ◽  
Dead Zones ◽  
Control Scheme

This paper investigates a novel adaptive output feedback decentralized control scheme for switched nonstrict feedback large-scale systems with unknown dead zones. A decentralized linear state observer is designed to estimate the unmeasurable states of subsystems. The dead zone inverse technique is used to compensate the effect of the unknown dead zone. A variable separation approach is applied to deal with the nonstrict feedback problem. Moreover, dynamic surface control and minimal parameter learning technology are adopted to reduce the computation burden. The proof of stability and the arbitrary switching are obtained by the common Lyapunov method. Finally, simulation results are given to show the effectiveness of the proposed control scheme.

scholarly journals Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Huanqing Wang ◽  
Qi Zhou ◽  
Xuebo Yang ◽  
Hamid Reza Karimi
Keyword(s):  
Neural Control ◽  
Large Scale ◽  
Controller Design ◽  
Dead Zone ◽  
Design Procedure ◽  
Rbf Neural Networks ◽  
Large Scale Systems ◽  
Dead Zones ◽  
Stabilization Control ◽  
Control Scheme

The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF) neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points). It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

scholarly journals Adaptive Decentralized Output Feedback Tracking Control for Large-Scale Interconnected Systems with Time-Varying Output Constraints

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Min Wan ◽  
Yanxia Yin
Keyword(s):  
Output Feedback ◽  
Large Scale ◽  
Dynamic Surface Control ◽  
Learning Technologies ◽  
Interconnected Systems ◽  
Time Varying ◽  
Dynamic Surface ◽  
Control Scheme ◽  
Output Constraints ◽  
Two Parameters

This paper investigates a novel adaptive output feedback decentralized control scheme for nonstrict feedback large-scale interconnected systems with time-varying constraints. A decentralized linear state observer is designed to estimate the unmeasurable states of subsystems. Time-varying barrier Lyapunov functions are designed to ensure outputs are not violating constraints. A variable separation approach is applied to deal with the nonstrict feedback problem. Moreover, dynamic surface control and minimal parameter learning technologies are adopted to reduce the computation burden, and there are only two parameters for every subsystem to be updated online. The proof of stability is obtained by the Lyapunov method. Finally, simulation results are given to show the effectiveness of the proposed control scheme.

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