Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics

作者：Zhang Tianping^*; Shi Xiaocheng; Zhu Qing; Yang Yuequan

来源：Neurocomputing, 2013, 121: 290-297.

DOI：10.1016/j.neucom.2013.04.023

• Adaptive control
• Neural networks
• Pure-feedback nonlinear systems
• Unmodeled dynamics
• Nussbaum function

In this paper, robust adaptive control is proposed for a class of pure-feedback nonlinear systems with unmodeled dynamics and unknown gain signs using radial basis function neural networks (RBFNNs). Dynamic uncertainties are dealt with using a dynamic signal. The unknown virtual gain signs are solved using the Nussbaum functions. Using mean value theorem and Youngs inequality, only one learning parameter needs to be tuned online at each step of recursion. It is proved that the proposed design scheme can guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results demonstrate the effectiveness of the proposed approach.

• 出版日期2013-12-9
• 单位扬州大学