Tutsoy Onder, Barkana Duygun Erol
Adana Alparslan Turkes Science and Technology University, Electrical and Electronic Engineering, Adana, Turkey.
Yeditepe University, Electrical and Electronic Engineering, Istanbul, Turkey.
ISA Trans. 2021 Dec;118:106-115. doi: 10.1016/j.isatra.2021.02.006. Epub 2021 Feb 8.
Development of practical control approaches for the under-actuated chaotic systems such as the robot manipulators are challenging due to the unpredictable character of the chaotic dynamics, and the inevitable real-time application properties like delays, saturations, and uncertainties In this paper, we propose a model free digital adaptive control approach, which considers the time delay of the control signal, actuator saturation, and non-parametric uncertainties, for an under-actuated manipulator. We also develop a chaos control to learn the unbiased and smooth digital control policy inside the chaotic regions of the continuous time under-actuated manipulator. We perform real-time experiments in a dynamic environment with the proposed digital adaptive control. Then we compare the results of the learning and control with and without chaos control. We observe that the proposed model free adaptive control approach can accurately learn both the long-term predictor and unbiased control policy even in the chaotic regions of the under-actuated robot manipulator.
对于诸如机器人操纵器之类的欠驱动混沌系统而言，开发实用的控制方法颇具挑战性，这是因为混沌动力学具有不可预测的特性，以及存在诸如延迟、饱和及不确定性等不可避免的实时应用属性。在本文中，我们针对一个欠驱动操纵器提出了一种无模型数字自适应控制方法，该方法考虑了控制信号的时间延迟、执行器饱和以及非参数不确定性。我们还开发了一种混沌控制，以在连续时间欠驱动操纵器的混沌区域内学习无偏且平滑的数字控制策略。我们在所提出的数字自适应控制下，在动态环境中进行实时实验。然后，我们比较有无混沌控制时的学习与控制结果。我们观察到，所提出的无模型自适应控制方法即使在欠驱动机器人操纵器的混沌区域内，也能准确学习长期预测器和无偏控制策略。
