Chen Weisheng, Li Junmin
IEEE Trans Syst Man Cybern B Cybern. 2008 Feb;38(1):258-66. doi: 10.1109/TSMCB.2007.904544.
An adaptive backstepping neural-network control approach is extended to a class of large-scale nonlinear output-feedback systems with completely unknown and mismatched interconnections. The novel contribution is to remove the common assumptions on interconnections such as matching condition, bounded by upper bounding functions. Differentiation of the interconnected signals in backstepping design is avoided by replacing the interconnected signals in neural inputs with the reference signals. Furthermore, two kinds of unknown modeling errors are handled by the adaptive technique. All the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded, and the tracking errors are proved to converge to a small residual set around the origin. The simulation results illustrate the effectiveness of the control approach proposed in this correspondence.
一种自适应反步神经网络控制方法被扩展到一类具有完全未知且不匹配互联项的大规模非线性输出反馈系统。新颖之处在于去除了关于互联项的常见假设，如匹配条件、由上界函数界定等。通过用参考信号替换神经输入中的互联信号，避免了反步设计中互联信号的求导。此外，自适应技术处理了两种未知建模误差。保证所有闭环信号半全局一致最终有界，并且证明跟踪误差收敛到原点附近的一个小残差集。仿真结果说明了本通信中提出的控制方法的有效性。
