IEEE Trans Neural Netw Learn Syst. 2017 Feb;28(2):294-307. doi: 10.1109/TNNLS.2015.2506267. Epub 2015 Dec 24.
This paper mainly aims at the problem of adaptive quantized control for a class of uncertain nonlinear systems preceded by asymmetric actuator backlash. One challenging problem that blocks the construction of our control scheme is that the real control signal is wrapped in the coupling of quantization effect and nonsmooth backlash nonlinearity. To resolve this challenge, this paper presents a two-stage separation approach established on two new technical components, which are the approximate asymmetric backlash model and the nonlinear decomposition of quantizer, respectively. Then the real control is successfully separated from the coupling dynamics. Furthermore, by employing the neural networks and adaptation method in control design, a quantized controller is developed to guarantee the asymptotic convergence of tracking error to an adjustable region of zero and uniform ultimate boundedness of all closed-loop signals. Eventually, simulations are conducted to support our theoretical results.
本文主要针对一类具有非对称执行器间隙的不确定非线性系统的自适应量化控制问题。阻碍我们控制方案构建的一个挑战性问题是，实际控制信号被包裹在量化效应和非光滑间隙非线性的耦合中。为了解决这个挑战，本文提出了一种基于两个新技术组件的两阶段分离方法，分别是近似非对称间隙模型和量化器的非线性分解。然后，成功地将实际控制从耦合动力学中分离出来。此外，通过在控制设计中使用神经网络和自适应方法，开发了一个量化控制器，以保证跟踪误差收敛到可调节的零区域，并且所有闭环信号具有一致的有界性。最终，通过仿真验证了我们的理论结果。
