The aim of this work is to utilize an adaptive decentralized control method called virtual decomposition control (VDC) to control the orientation and position of the end-effector of a 7 degrees of freedom (DoF) right-hand upper-limb exoskeleton. The prevailing adaptive VDC approach requires tuning of 13n adaptation gains along with 26n upper and lower parameter bounds, where n is the number of rigid bodies. Therefore, utilizing the VDC scheme to control high DoF robots like the 7-DoF upper-limb exoskeleton can be an arduous task. In this paper, a new adaptation function, so-called natural adaptation law (NAL), is employed to eliminate these burdens from VDC, which results in reducing all 13n gains to one and removing dependency on upper and lower bounds. In doing so, VDC-based dynamic equations are restructured, and inertial parameter vectors are made compatible with NAL. Then, the NAL adaptation function is exploited to design a new adaptive VDC scheme. This novel adaptive VDC approach ensures physical consistency conditions for estimated parameters with no need for upper and lower bounds. Finally, the asymptotic stability of the algorithm is proved with the virtual stability concept and the accompanying function. The experimental results are utilized to demonstrate the excellent performance of the proposed new adaptive VDC scheme.
翻译:这项工作的目的是使用一种适应性分散控制方法,称为虚拟分解控制(VDC),以控制7度自由(DoF)右手右上升升平exoskeleton7度自由(DoF)的终效器的方向和位置。现行适应性VDC方法要求调整13n的适应收益和26n的上下参数界限,其中n为硬体体数。因此,利用VDC计划控制高度DoF机器人,如7度多佛上升平流外向骨骼(VDC),可能是一项艰巨的任务。在本文中,采用了一种新的适应功能,即所谓的自然适应法(NAL),以消除VDC的这些负担,从而将所有13度收益减少到1度,并消除对上下界限的依赖。为此,VDC的动态方程式进行了调整,惯性参数矢量与NAL兼容。然后,NAL适应功能被用来设计一个新的适应性VDC计划。这种新型适应性VDC方法确保了估计参数的物理一致性条件,而无需对上下限和下限的虚拟适应性法律概念。最后证明,VDC的拟议的适应性功能是已加以利用的。