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 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 Adaptive Fuzzy Control via State-Dependent Function for Nonlinear Stochastic Large-Scale Systems Subject to Dead Zones

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chang-Qi Zhu ◽  
Lei Liu
Keyword(s):  
Fuzzy Control ◽  
Large Scale ◽  
Dead Zone ◽  
Adaptive Fuzzy Control ◽  
Closed Loop System ◽  
Large Scale Systems ◽  
Adaptive Fuzzy ◽  
State Dependent ◽  
Control Scheme ◽  
Robust Adaptive

This paper concentrates on the adaptive fuzzy control problem for stochastic nonlinear large-scale systems with constraints and unknown dead zones. By introducing the state-dependent function, the constrained closed-loop system is transformed into a brand-new system without constraints, which can realize the same control objective. Then, fuzzy logic systems (FLSs) are used to identify the unknown nonlinear functions, the dead zone inverse technique is utilized to compensate for the dead zone effect, and a robust adaptive fuzzy control scheme is developed under the backstepping frame. Based on the Lyapunov stability theory, it is proved ultimately that all signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood of the origin. Finally, an example based on an actual system is given to verify the effectiveness of the proposed control scheme.

A New Variable Structure Control Scheme for Mismatched Uncertain Large Scale Systems

2015 ◽  
Vol 789-790 ◽  
pp. 1005-1010
Author(s):  
Yao Wen Tsai ◽  
Phan Van Duc ◽  
Van Van Huynh
Keyword(s):  
Large Scale ◽  
Controller Design ◽  
Sliding Mode ◽  
Variable Structure Control ◽  
Variable Structure ◽  
Order System ◽  
Large Scale Systems ◽  
Structure Control ◽  
Control Scheme ◽  
Output Information

In this paper, a new decentralized adaptive output feedback variable structure control scheme is designed for mismatched uncertain large-scale systems where the exogenous disturbance is unknown. The proposed approach uses output information completely in sliding surface and controller design. Therefore, conservatism is reduced and robustness is enhanced. Furthermore, the reduce order system in sliding mode is asymptotically stable under certain conditions. Finally, a numerical example is used to demonstrate the efficacy on the method.

Adaptive decentralized tracking control of a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network

2019 ◽  
Vol 41 (16) ◽  
pp. 4499-4510 ◽  
Author(s):  
Yu-Qun Han ◽  
Shan-Liang Zhu ◽  
De-Yu Duan ◽  
Lei Chu ◽  
Shu-Guo Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
Large Scale ◽  
Controller Design ◽  
Dead Zone ◽  
Computational Cost ◽  
Tracking Errors ◽  
Control Approach ◽  
Control Scheme

In this paper, an adaptive decentralized control approach is proposed for a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network. Firstly, the dead-zone outputs are firstly represented as simple linear systems with a static time-varying gain and bounded disturbance by introducing characteristic function. Secondly, in the controller design, neural networks are utilized to approximate the unknown nonlinear functions. Thirdly, an adaptive decentralized tracking control approach is constructed via backstepping design technique. It is shown that the proposed control approach can assure that all the signals of the closed-loop system semi-globally uniformly ultimately bounded and the tracking errors finally converge to a small domain around the origin. The proposed method can get precise tracking results with low computational cost, and have a good real-time performance and convergence. Finally, two examples are given to demonstrate the effectiveness of the proposed control scheme.

scholarly journals Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Shifen Shao ◽  
Kaisheng Zhang ◽  
Jun Li ◽  
Jirong Wang
Keyword(s):  
Neural Control ◽  
Dead Zone ◽  
Tracking Error ◽  
Transient Behavior ◽  
Robotic Manipulators ◽  
System Stability ◽  
Control Effect ◽  
Tracking Errors ◽  
Control Scheme ◽  
Funnel Control

This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone. A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics. An improved funnel function is designed to guarantee the transient behavior of the tracking error. The proposed funnel function can release the assumption on the conventional funnel control. Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary. The closed-loop system stability is proved via Lyapunov function. Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.

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