Abstract
Impedance and Admittance Control are two well-known controllers to accomplish the same goal: the regulation of the mechanical impedance of manipulators interacting dynamically with the environment. However, they both are affected by a strong limitation deriving from their fixed causality, which causes their inability to provide good performance over a large spectrum of environment stiffnesses. In this paper an adaptive hybrid system framework is proposed to unify Impedance and Admittance formulations and consequently overcome this limit. Indeed, the hybrid framework interpolates the opposite performance and stability characteristics of the above-mentioned impedance-based control strategies leading to a family of controllers with intermediate properties, and thus suitable for several conditions. Moreover, the adaptivity allows the hybrid system to operate properly in an environment characterized by unknown and even time-varying stiffness. Especially, the work focuses on the development of this latter aspect and an adaptive solution based on a feedforward Neural Network is presented. The effectiveness of the novel control strategy is demonstrated by means of numerical simulations.
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Acknowledgements
The first two authors would like to thank professor Ranjan Mukherjee, from Michigan State University, for having hosted them during the research, and dr. Christian Ott, from DLR, for the useful discussions about the topic presented in the paper.
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Cavenago, F., Voli, L. & Massari, M. Adaptive Hybrid System Framework for Unified Impedance and Admittance Control. J Intell Robot Syst 91, 569–581 (2018). https://doi.org/10.1007/s10846-017-0732-1
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DOI: https://doi.org/10.1007/s10846-017-0732-1