Adaptive Neural Control of a Class of Stochastic Nonlinear Uncertain Systems With Guaranteed Transient Performance

• Published in: IEEE transactions on cybernetics Vol. 50; no. 7; pp. 2971 - 2981
• Main Authors: Wang, Jianhui, Liu, Zhi, Zhang, Yun, Chen, C. L. Philip, Lai, Guanyu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive systems; Approximation error; Backstepping; Computer Simulation; Control stability; Network control; Neural networks; Neural Networks, Computer; neutral network; Nonlinear control; Nonlinear Dynamics; Nonlinear systems; stochastic disturbance; Stochastic Processes; Stochastic systems; Time-varying systems; Tracking errors; Transient analysis; Transient performance
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2019.2891265

In this paper, an adaptive neural network control for stochastic nonlinear systems with uncertain disturbances is proposed. The neural network is considered to approximate an uncertain function in a nonlinear system. And computational burden in operation is reduced by handling the norm of the neural-network vector. However, it will arise chattering issue, which is a challenge to avoid it from the symbolic operation. Further, traditional schemes often view error of estimate as bounded constant, but it is a time-varying function exactly, which may lead control schemes cannot conform to practical situation and guarantee stability of systems. Thus, backstepping technology and the neural network technology combined to stabilize stochastic nonlinear systems together to handle the aforementioned issues. It is proved that the proposed control scheme can guarantee the satisfactory asymptotic convergence performance and predetermined transient tracking error performance. From simulation results, the proposed control scheme is verified that can guarantee the satisfactory effectiveness.