Further Results on Adaptive Stabilization of High-Order Stochastic Nonlinear Systems Subject to Uncertainties

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 1; pp. 225 - 234
• Main Authors: Min, Huifang, Xu, Shengyuan, Gu, Jason, Zhang, Baoyong, Zhang, Zhengqiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive systems; Artificial neural networks; Control systems; Delays; Feedback; Feedback control; high-order stochastic nonlinear systems; Neural networks; Nonlinear systems; Parameter uncertainty; Perturbation; Radial basis function; radial basis function neural network (RBF NN); Stochastic processes; Stochastic systems; Stochasticity; time delay; Uncertainty; unknown control gain
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2019.2900339

This paper concerns the adaptive state-feedback control for a class of high-order stochastic nonlinear systems with uncertainties including time-varying delay, unknown control gain, and parameter perturbation. The commonly used growth assumptions on system nonlinearities are removed, and the adaptive control technique is combined with the sign function to deal with the unknown control gain. Then, with the help of the radial basis function neural network approximation approach and Lyapunov-Krasovskii functional, an adaptive state-feedback controller is obtained through the backstepping design procedure. It is verified that the constructed controller can render the closed-loop system semiglobally uniformly ultimately bounded. Finally, both the practical and numerical examples are presented to validate the effectiveness of the proposed scheme.