Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems. (November 2021)

• Record Type: Journal Article
• Authors: Liu, Yongchao; Zhu, Qidan
• Is Part Of: Neural networks. Volume 143(2021)
• Page Start: 283
• Page End: 290
• Publication Date: 2021-11
• Subjects: Asymptotic tracking control -- Backstepping algorithm -- Neural network -- Stochastic nonlinear systems; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.06.011
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: The adaptive neural network asymptotic tracking control issue of nonstrict feedback stochastic nonlinear systems is studied in our article by adopting backstepping algorithm. Compared with the existing research, the hypothesis about unknown virtual control coefficients (UVCC) is overcome in the control design. By using the bound estimation scheme and some smooth functions, associating with approximation-based neural network, the asymptotic tracking controller is recursively constructed. With the aid of Lyapunov function and beneficial inequalities, the asymptotic convergence character and stability with stochastic disturbance and unknown UVCC can be ensured. Finally, the theoretical finding is verified via a simulation example.