Exoskeleton robots found application in neurorehabilitation, telemanipulation, and power augmentation. The human–robot attachment system of an exoskeleton should transmit all the interaction forces while keeping the anatomical and robotic joint axes aligned. Existing attachment concepts were bounding the performance of modern exoskeletons due to insufficient stiffness for high-performance force control, time-consuming adaption processes, and/or bulkiness. Therefore, we developed an augmented attachment system for a recent fully actuated nine-degree-of-freedom upper limb exoskeleton. The proposed system was compared to a conventional solution in a case study with four participants. The proposed attachment system lowered the relative motion between the human and the robot under static loads for all defined landmarks by 45% on average. The occurrence of undesired contacts in the trials was mitigated by 74%, thus improving conditions for closed-loop force control. Furthermore, the proposed system adapted better to the user's anatomy facilitating more accurate alignment and less obstruction. On average, self-attachment took$\mathbf {43(8.3)}$$\mathrm{s}$to don(doff). Thereby, the alignment of anatomic landmarks had typically less than 15 mm offset to a thorough expert alignment, making self-attachment eligible. The augmented attachment system and the insights gained by the case study are expected to enable improvement of the physical human–robot interaction of exoskeletons.

It is well known that model order reduction for circuits with many terminals remains a challenging problem. One reason is that existing approaches are based on a centralized framework, in which each input-output pair is implicitly assumed to be equally interacted and the matrix-valued transfer function is assumed to be fully populated. In this paper, we attempt to address this long-standing problem using a decentralized model order reduction scheme, in which a multi-input multi-output system is decoupled into a number of subsystems and each subsystem corresponds to one output and several dominant inputs. The decoupling process is based on the relative gain array, which measures the degree of interaction of each input-output pair. For each decoupled subsystem, passive reduction can be easily achieved using existing reduction techniques. The proposed method is suitable for resistance-dominant interconnects such as on-chip power grids, substrate planes where extremely compact models can be obtained. Simulation results demonstrate the advantage of the proposed method compared to the existing approaches.

Model order reduction is an efficient technique to reduce the system complexity while producing a good approximation of the input-output behavior. However, the efficiency of reduction degrades as the number of ports increases, which remains a long-standing problem. The reason for the degradation is that existing approaches are based on a centralized framework, where each input-output pair is implicitly assumed to be equally interacted and the matrix-valued transfer function has to be assumed to be fully populated. In this paper, a decentralized model order reduction scheme is proposed, where a multi-input multi-output (MIMO) system is decoupled into a number of subsystems and each subsystem corresponds to one output and several dominant inputs. The decoupling process is based on the relative gain array (RGA), which measures the degree of interaction of each input-output pair. Our experimental results on a number of interconnect circuits show that most of the input-output interactions are usually insignificant, which can lead to extremely compact models even for systems with massive ports. The reduction scheme is very amenable for parallel computing as each decoupled subsystem can be reduced independently.