A combined NN and dynamic gain-based approach to further stabilize nonlinear time-delay systems

作者：Duan, Na^*; Min, Huifang; Shu, Zhan; Wang, Shuliang

来源：Neural Computing & Applications, 2019, 31(7): 2183-2193.

DOI：10.1007/s00521-017-3180-8

• High-order nonlinear systems
• Time delay
• Dynamic gain-based backstepping
• RBF NN
• State-feedback

This paper focuses on the state-feedback control problem for a class of high-order nonlinear systems with unknown time delay and control coefficients. Based on a novel dynamic gain-based backstepping technique and radial basis function neural network (RBF NN) approximation approach, the restrictions on high-order and nonlinearities are removed or further relaxed. Under these weaker conditions, a smooth state-feedback controller is skillfully constructed with only one adaptive parameter. In addition, the knowledge of time delay, NN nodes and weights is not necessary to be known a priori. It is proven that the designed controller can render the closed-loop system be semi-globally uniformly ultimately bounded. Finally, both practical and numerical examples are shown to demonstrate the effectiveness of the proposed scheme.

• 出版日期2019-7
• 单位南京理工大学