ArticleFrontiers in computational neuroscience2014
Mini-max feedback control as a computational theory of sensorimotor control in the presence of structural uncertainty.
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Human Steering Control Under Unpredictable Disturbances.The European journal of neuroscience · 2026Article
- Increased muscle coactivation is linked with fast feedback control when reaching in unpredictable visual environments.iScience · 2024Article
- Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control.PLoS computational biology · 2024Article
- Basketball free-throw training with augmented reality-based optimal shot trajectory for novice shooters.Scientific reports · 2024Article
- Costs of position, velocity, and force requirements in optimal control induce triphasic muscle activation during reaching movement.Scientific reports · 2021Article
- Article
- Robust Control in Human Reaching Movements: A Model-Free Strategy to Compensate for Unpredictable Disturbances.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Optimal feedback control to describe multiple representations of primary motor cortex neurons.Journal of computational neuroscience · 2017Article
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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.
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