Robust Sliding Mode Control for Robot Manipulators
Robust Sliding Mode Control for Robot Manipulators
复制标题
DOI：
10.1109/tie.2010.2062472
复制
发表时间：
2011-06
影响因子：
7.7
通讯作者：
S. Islam;P. X. Liu
中科院分区：
文献类型：
--
作者：
S. Islam;P. X. Liu
在面对大规模的参数不确定性，单模型（SM）为基础的滑模控制（SMC）方法需要高增益的观察器，控制器，并适应，以实现满意的跟踪性能。基于高增益设计的主要实际问题是它放大了输入和输出干扰，并激发了隐藏的未建模动态，导致跟踪性能差。在本文中，多模型/控制为基础的SMC技术，提出了减少的参数不确定性的水平，以减少跨导控制器的增益。为此，我们均匀地分裂成一个有限数量的较小的紧凑子集的未知参数的紧凑集。然后，我们设计一个候选SMC对应于这些较小的子集。的李雅普诺夫函数候选人的衍生物被用作重置标准，以确定一个候选人的模型，接近工厂在每个时刻的时间。其关键思想是允许传统的自适应滑模控制设计的参数估计被重置到一个模型，最好的估计植物之间的一组有限的候选模型。所提出的方法进行评估2自由度机器人操作器，以证明理论发展的有效性。
In the face of large-scale parametric uncertainties, the single-model (SM)-based sliding mode control (SMC) approach demands high gains for the observer, controller, and adaptation to achieve satisfactory tracking performance. The main practical problem of having high-gain-based design is that it amplifies the input and output disturbance as well as excites hidden unmodeled dynamics, causing poor tracking performance. In this paper, a multiple model/control-based SMC technique is proposed to reduce the level of parametric uncertainty to reduce observer-controller gains. To this end, we split uniformly the compact set of unknown parameters into a finite number of smaller compact subsets. Then, we design a candidate SMC corresponding to each of these smaller subsets. The derivative of the Lyapunov function candidate is used as a resetting criterion to identify a candidate model that approximates closely the plant at each instant of time. The key idea is to allow the parameter estimate of conventional adaptive sliding mode control design to be reset into a model that best estimates the plant among a finite set of candidate models. The proposed method is evaluated on a 2-DOF robot manipulator to demonstrate the effectiveness of the theoretical development.