Adaptive neural control of non-affine pure-feedback non-linear systems with input nonlinearity and perturbed uncertainties

作者：Zhang Tian Ping^*; Zhu Qing; Yang Yue Quan

来源：International Journal of Systems Science, 2012, 43(4): 691-706.

DOI：10.1080/00207721.2010.519060

• adaptive control
• neural networks
• dynamic surface control
• pure-feedback non-linear systems
• input non-linearity
• Nussbaum function

In this article, two robust adaptive control schemes are investigated for a class of completely non-affine pure-feedback non-linear systems with input non-linearity and perturbed uncertainties using radial basis function neural networks (RBFNNs). Based on the dynamic surface control (DSC) technique and using the quadratic Lyapunov function, the explosion of complexity in the traditional backstepping design is avoided when the gain signs are known. In addition, the unknown virtual gain signs are dealt with using the Nussbaum functions. Using the mean value theorem and Young's inequality, only one learning parameter needs to be tuned online at each step of recursion. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results verify the effectiveness of the proposed approach.

• 出版日期2012
• 单位扬州大学