Fractional order admittance control for physical human-robot interaction

Abstract

Abstract-In physical human-robot interaction (pHRI), the cognitive skill of a human is combined with the accuracy, repeatability and strength of a robot. While the promises and potential outcomes of pHRI are glamorous, the control of such coupled systems is challenging in many aspects. In this paper, we propose a new controller, fractional order admittance controller, for pHRI systems. The stability analysis of the new control system with human in-the-loop is performed and the interaction performance is investigated experimentally with 10 subjects during a task imitating a contact with a stiff environment. The results show that the fractional order controller is more robust than the standard admittance controller and helps to reduce the human effort in task execution.

Towards collaborative drilling with a cobot using admittance controller

“…Alternatively, knowing that non-passive systems are not necessarily unstable (Buerger et al, 2001), less conservative controllers can be designed by utilizing linear models of robot, human, and environment, and the effect of modeling parameters on the stability of coupled system can be investigated. Tsumugiwa et al (2004) carried out root locus analysis for a pHRI task and found that an increase in the environment stiffness is the primary reason for instability, which was also supported by later studies (Aydin et al, 2017(Aydin et al, , 2018Ferraguti et al, 2019). They argued that when the environment stiffness is high, the system can be stabilized by increasing the damping parameter of the admittance controller.…”

“…Following the cobot characterization, the methods to examine the stability and transparency of our pHRI system are presented. In our earlier work (Aydin et al, 2017(Aydin et al, , 2018, we investigated integer and fractional order admittance controllers and compared their stability characteristics for typical values of human arm impedance. On the other hand, as our objective is to present the steps towards developing an optimal controller in this study, a thorough stability analysis is conducted by considering the combinations of extreme bounds of human arm and environment impedances.…”

A Variable-Fractional Order Admittance Controller for pHRI

“…We have proposed a fractional order admittance controller (FOAC) for pHRI systems in [5] and [19]. This control scheme relies on the fractional order calculus, which allows the use of integrators/differentiators of arbitrary orders.…”

Detecting Human Motion Intention during pHRI Using Artificial Neural Networks Trained by EMG Signals

“…In particular, if the intended movement direction is detected accurately, then the cobot may be constrained to move along that direction only. For the implementation of the path following scenario discussed above, we integrate the admittance control architecture suggested in [21]- [23] with the proposed direction classifier. So, the controller enables movements along the intended directions of human arm only that are estimated using ANN, trained by EMG signals alone (see Fig.…”