基于神经网络的一类非仿射系统的自适应控制

Adaptive control of a class of nonaffine systems using neural networks.

作者信息

Yang Bong-Jun, Calise Anthony J

机构信息

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

出版信息

IEEE Trans Neural Netw. 2007 Jul;18(4):1149-59. doi: 10.1109/TNN.2007.899253.

DOI:10.1109/TNN.2007.899253

PMID:17668667

Abstract

A neural control synthesis method is considered for a class of nonaffine uncertain single-input-single-output (SISO) systems. The method eliminates a fixed-point assumption and does not assume boundedness on the time derivative of a control effectiveness term. One or the other of these assumptions exist in earlier papers on this subject. Using Lyapunov's direct method, it is shown that all the signals of the closed-loop system are uniformly ultimately bounded, and that the tracking error converges to an adjustable neighborhood of the origin. Simulation with a Van Der Pol equation with nonaffine control terms illustrates the approach.

摘要

针对一类非仿射不确定单输入单输出（SISO）系统，考虑了一种神经控制综合方法。该方法消除了不动点假设，并且不假设控制有效性项的时间导数有界。关于该主题的早期论文中存在这两个假设中的一个。利用李雅普诺夫直接法，证明了闭环系统的所有信号都是一致最终有界的，并且跟踪误差收敛到原点的一个可调节邻域。对具有非仿射控制项的范德波尔方程进行仿真说明了该方法。