Liu Dong, Yang Guang-Hong
IEEE Trans Neural Netw Learn Syst. 2019 Jul;30(7):2222-2230. doi: 10.1109/TNNLS.2018.2881205. Epub 2018 Dec 7.
This paper studies the data-driven prescribed performance control (PPC) problem for a class of discrete-time nonlinear systems in the presence of tracking error constraints. By using the equivalent dynamic linearization technique and constructing a novel transformed error strategy, an adaptive integral sliding mode controller is designed such that the tracking error converges to a predefined neighborhood. Meanwhile, the presented control scheme can effectively ensure that the convergence rate is less than a predefined value and maximum overshoot is not smaller than a preselected constant. In addition, better tracking performance can be achieved by regulating the design parameters appropriately, which is more preferable in the practical application. Contrary to the existing PPC results, the new proposed control law does not use either the plant structure or any knowledge of system dynamics. The efficiency of the proposed control approach is shown with two simulated examples.
本文研究了一类存在跟踪误差约束的离散时间非线性系统的数据驱动的规定性能控制（PPC）问题。通过使用等效动态线性化技术并构造一种新颖的变换误差策略，设计了一种自适应积分滑模控制器，使得跟踪误差收敛到一个预定义的邻域。同时，所提出的控制方案能够有效地确保收敛速率小于一个预定义的值，并且最大超调量不小于一个预先选定的常数。此外，通过适当地调节设计参数可以实现更好的跟踪性能，这在实际应用中更具优势。与现有的PPC结果相反，新提出的控制律既不使用被控对象结构也不使用任何系统动力学知识。通过两个仿真例子展示了所提出控制方法的有效性。
