Globally decentralized adaptive backstepping neural network tracking control for unknown nonlinear interconnected systems

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Weisheng Chen ◽  
Junmin Li
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Interconnected Systems ◽  
Adaptive Backstepping

scholarly journals Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1152
Author(s):  
Yang Li ◽  
Jianhua Zhang ◽  
Xinli Xu ◽  
Cheng Siong Chin
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Fixed Time ◽  
Convergence Time ◽  
Interconnected Systems ◽  
Adaptive Backstepping ◽  
Step Procedure ◽  
Control Scheme ◽  
Lyapunov Stability Analysis ◽  
Unknown System

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed. An adaptive backstepping technique is used to address unknown system uncertainties in the fixed-time settings. Neural networks are used to identify the unknown uncertainties. The study shows that, under the proposed control scheme, each state in the system can converge into small regions near zero with fixed-time convergence time via Lyapunov stability analysis. Finally, the simulation example is presented to demonstrate the effectiveness of the proposed approach. A step-by-step procedure for engineers in industry process applications is proposed.

Decentralized adaptive tracking control for high-order interconnected stochastic nonlinear time-varying delay systems with stochastic input-to-state stable inverse dynamics by neural networks

2019 ◽  
Vol 41 (13) ◽  
pp. 3612-3625 ◽  
Author(s):  
Wang Qian ◽  
Wang Qiangde ◽  
Wei Chunling ◽  
Zhang Zhengqiang
Keyword(s):  
Neural Networks ◽  
Tracking Control ◽  
Large Scale ◽  
Inverse Dynamics ◽  
Small Neighborhood ◽  
High Order ◽  
Interconnected Systems ◽  
Delay Systems ◽  
Rbf Neural Networks ◽  
Stochastic Input

The paper solves the problem of a decentralized adaptive state-feedback neural tracking control for a class of stochastic nonlinear high-order interconnected systems. Under the assumptions that the inverse dynamics of the subsystems are stochastic input-to-state stable (SISS) and for the controller design, Radial basis function (RBF) neural networks (NN) are used to cope with the packaged unknown system dynamics and stochastic uncertainties. Besides, the appropriate Lyapunov-Krosovskii functions and parameters are constructed for a class of large-scale high-order stochastic nonlinear strong interconnected systems with inverse dynamics. It has been proved that the actual controller can be designed so as to guarantee that all the signals in the closed-loop systems remain semi-globally uniformly ultimately bounded, and the tracking errors eventually converge in the small neighborhood of origin. Simulation example has been proposed to show the effectiveness of our results.

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