Abstract
We propose a mini-max feedback control (MMFC) model as a robust approach to human motor control under conditions of uncertain dynamics, such as structural uncertainty. The MMFC model is an expansion of the optimal feedback control (OFC) model. According to this scheme, motor commands are generated to minimize the maximal cost, based on an assumption of worst-case uncertainty, characterized by familiarity with novel dynamics. We simulated linear dynamic systems with different types of force fields–stable and unstable dynamics–and compared the performance of MMFC to that of OFC. MMFC delivered better performance than OFC in terms of stability and the achievement of tasks. Moreover, the gain in positional feedback with the MMFC model in the unstable dynamics was tuned to the direction of instability. It is assumed that the shape modulations of the gain in positional feedback in unstable dynamics played the same role as that played by end-point stiffness observed in human studies. Accordingly, we suggest that MMFC is a plausible model that predicts motor behavior under conditions of uncertain dynamics.
Highlights
It is necessary to interact with various environments to learn how to use tools and to participate in unfamiliar sports, such as tennis and swimming
In rotated divergent force field (rDF), the lateral deviancy affected the vertical distance between the target hand positions through the feedback gain, and the task required more motor effort to reach the same distance to divergent force field (DF) because each actuator acted on only the x- or y-axis
In this study, mini-max feedback control (MMFC) is presented as an extension of optimal feedback control (OFC) for use as a robust control technique
Summary
It is necessary to interact with various environments to learn how to use tools and to participate in unfamiliar sports, such as tennis and swimming. An internal model can compensate for both stable and unstable dynamics, mechanisms have been identified for adapting to different approaches (Franklin et al, 2003b; Osu et al, 2003). In an unstable environment, the inverse dynamics model functions in parallel with an impedance controller to compensate for a consistent perturbing force (Osu et al, 2003). It has been suggested that the impedance controller assists in the formation of the inverse dynamics model and contributes to improved stability (Franklin et al, 2003b) Both approaches are used selectively and combined in accordance with environmental dynamics
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