Chen Weisheng, Jiao Licheng
Department of Applied Mathematics, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China.
IEEE Trans Neural Netw. 2010 Feb;21(2):345-51. doi: 10.1109/TNN.2009.2038999. Epub 2010 Jan 12.
This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief.
本简报探讨了一类严格反馈系统的自适应神经网络跟踪控制设计问题，该系统存在未知的时变干扰，这些干扰以已知周期非线性地出现在未知函数中。将多层神经网络（MNN）和傅里叶级数展开（FSE）结合成一种新型逼近器，以对系统中的每个不确定性进行建模。结合动态表面控制（DSC）方法和积分型李雅普诺夫函数（ILF）技术来设计控制算法。保证了所有闭环信号的最终一致有界性。证明跟踪误差收敛到原点附近的一个小残差集。提供了两个仿真例子来说明本简报中提出的控制方案的可行性。
