Fractional order identification of human arm dynamics: Preliminary results

Abstract

Recently, control approaches for human-like behaviour have been attracting considerable attention in the field of surgery robotics. To this respect, precise dynamic models of the human arm are required to give the surgeon the physical feeling of working with a human assistant rather than a machine, which will result in a safer physical interaction. Musculo-skeletal systems of several species, including human muscles, have been successfully modelled by fractional differential equations. This study presents fractional order closed-loop identification for estimating the dynamics of the human arm. In particular, fractional and integer order models are identified in the frequency domain from real experiments with human subjects, using continuous random force as input and position in angle as output. The results show that more general dynamic models, i.e., fractional order models, allow adequate frequency responses to be attained but with a smaller number of parameters. A comparison with different dynamic models of the human arm reported in the literature is also given to demonstrate the validity of the proposed models.