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

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.

Author(s):  
Yuxiang Wu ◽  
Tian Xu ◽  
Haoran Fang

This article investigates the command filtered adaptive neural tracking control for uncertain nonlinear time-delay systems subject to asymmetric time-varying full state constraints and actuator saturation. To stabilize such a class of systems, the radial basis function neural networks and the backstepping technique are used to structure an adaptive controller. The command filter is utilized to overcome the complexity explosion problem in backstepping. By employing the Lyapunov–Krasovskii functionals, the effect of time-delay is eliminated. The asymmetric time-varying barrier Lyapunov functions are designed to ensure full state constraint satisfaction. Moreover, the hyperbolic tangent function and an instrumental variable are introduced to deal with actuator saturation. All signals in the closed-loop system are proved to be bounded and the tracking error converges to a small neighborhood of the origin. Finally, two examples are provided to illustrate the effectiveness of the proposed method.


Sign in / Sign up

Export Citation Format

Share Document