High-Order Fuzzy Switching Neural Networks: Application to the Tracking Control of a Class of Uncertain SISO Nonlinear Systems

2009 ◽  
pp. 648-657
Author(s):  
Haris E. Psillakis
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Tracking Control ◽  
High Order

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.

Output tracking control of high-order nonlinear systems with dynamic uncertainties

2019 ◽  
Vol 42 (8) ◽  
pp. 1511-1520
Author(s):  
Zong-Yao Sun ◽  
Yu-Jie Gu ◽  
Qinghua Meng ◽  
Wei Sun ◽  
Zhen-Guo Liu
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Tracking Error ◽  
High Order ◽  
Approximation Technique ◽  
Zero Dynamic ◽  
Adding A Power Integrator ◽  
Output Tracking ◽  
Output Tracking Control ◽  
Dynamic Uncertainties

This paper investigates the output tracking control problem for a class of nonlinear systems with zero dynamic. On the basis of adding a power integrator method and approximation technique, an appropriate controller is proposed to guarantee that the tracking error turns to a preassigned neighborhood of the origin. The systems under investigation allow unmeasurable dynamic uncertainties, unknown nonlinear functions and unknown high-order terms. As an application, two examples are provided to illustrate the effectiveness of a control strategy.

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems

2017 ◽  
Vol 47 (10) ◽  
pp. 3088-3099 ◽  
Author(s):  
Xudong Zhao ◽  
Xinyong Wang ◽  
Guangdeng Zong ◽  
Xiaolong Zheng
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
High Order ◽  
Stochastic Nonlinear Systems
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