Xie Kan, Chen Ci, Lewis Frank L, Xie Shengli
IEEE Trans Neural Netw Learn Syst. 2018 Dec;29(12):6303-6312. doi: 10.1109/TNNLS.2018.2828315. Epub 2018 May 18.
In this paper, we propose an adaptive neural-network-based asymptotic control algorithm for a class of nonlinear systems subject to unknown actuator quantization. To this end, we exploit the sector property of the quantization nonlinearity and transform actuator quantization control problem into analyzing its upper bounds, which are then handled by a dynamic loop gain function-based approach. In our adaptive control scheme, there is only one parameter required to be estimated online for updating weights of neural networks. Within the framework of Lyapunov theory, it is shown that the proposed algorithm ensures that all the signals in the closed-loop system are ultimately bounded. Moreover, an asymptotic tracking error is obtained by means of introducing Barbalat's lemma to the proposed adaptive law.
在本文中，我们针对一类受未知执行器量化影响的非线性系统，提出了一种基于自适应神经网络的渐近控制算法。为此，我们利用量化非线性的扇区特性，将执行器量化控制问题转化为分析其上限，然后通过基于动态环路增益函数的方法来处理这些上限。在我们的自适应控制方案中，仅需在线估计一个参数以更新神经网络的权重。在李雅普诺夫理论框架内，结果表明所提出的算法可确保闭环系统中的所有信号最终有界。此外，通过将巴尔巴拉特引理引入所提出的自适应律，可得到渐近跟踪误差。
