Bio-mimetic Adaptive Force/Position Control Using Fractal Impedance

Recommended citation: Carlo Tiseo, Wolfgang Merkt, Keyhan Kouhkiloui Babarahmati, Wouter Wolfslag, Sethu Vijayakumar, and Michael Mistry. Bio-mimetic Adaptive Force/Position Control Using Fractal Impedance. Proc. IEEE RAS/EMBS International Conf. on Biomedical Robotics & Biomechatronics (BioRob), 2020.

The ability of animals to interact with complex dynamics is unmatched in robots. Especially important to the interaction performances is the online adaptation of body dynamics, which can be modeled as an impedance behaviour. However, the variable impedance controller still possesses a challenge in the current control frameworks due to the difficulties of retaining stability when adapting the controller gains. The fractal impedance controller has been recently proposed to solve this issue. However, it still has limitations such as sudden jumps in force when it starts to converge to the desired position and the lack of a force feedback loop. In this manuscript, two improvements are made to the control framework to solve these limitations. The force discontinuity has been addressed introducing a modulation of the impedance via a virtual antagonist that modulates the output force. The force tracking has been modeled after the parallel force/position controller architecture. In contrast to traditional methods, the fractal impedance controller enables the implementation of a search algorithm on the force feedback to adapt its behaviour on the external environment instead of on relying on \textit{a priori} knowledge of the external dynamics. Preliminary simulation results presented in this paper show the feasibility of the proposed approach, and it allows to evaluate the trade-off that needs to be made when relying on the proposed controller for interaction. In conclusion, the proposed method mimics the behaviour of an agonist/antagonist system adapting to unknown external dynamics, and it may find application in computational neuroscience, haptics, and interaction control.

[ pdf] [ video] [ presentation] [ DOI: 10.1109/BioRob49111.2020.9224377] [ arXiv]

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