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ABSTRACT: Our long-term goal is to find control principles to control robotic hands with dexterity and robustness comparable to that of the human hand. Here we explore a control strategy capable of accommodating the nonlinearities, high dimensionality and endogenous noise intrinsic to complex, tendon-driven biomechanical structures. We present the first stochastic optimal feedback controller (i.e., an iterative Linear Quadratic Gaussian controller) applied to a tendon-driven simulated robotic index finger model. In our model we take into account both the tendon network driving of the index finger, and we consider first-order muscle dynamics. Our feedback controller shows robustness against noise and perturbation of the dynamics. Moreover, it can also successfully overcome the nonlinearities intrinsic to the mechanics of the finger for large postural changes, and the need for non-negative control signals. Our simulations provide, for the first time, the complete time history of tendon tensions, lengths and velocities for the tasks of tapping with nonzero terminal velocities required for dynamic manipulation. We find that the optimal control of realistic tendon-driven systems fundamentally stretches current methods to their limits. To find a successful control strategy, the algorithm must overcome several critical challenges inherent to the control of tendon-driven fingers systems in which all uni-directional control commands can actuate all joints (either directly or through dynamic coupling). Therefore, all elements of the solution are interwoven including the tuning of the cost function, the dynamics of the plant, and the initial guesses for state and control trajectories.
American Control Conference (ACC), 2011; 08/2011
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ABSTRACT: We provide an overview of optimal control methods to nonlinear neuromuscular systems and discuss their limitations. Moreover we extend current optimal control methods to their application to neuromuscular models with realistically numerous musculotendons; as most prior work is limited to torque-driven systems. Recent work on computational motor control has explored the used of control theory and estimation as a conceptual tool to understand the underlying computational principles of neuromuscular systems. After all, successful biological systems regularly meet conditions for stability, robustness and performance for multiple classes of complex tasks. Among a variety of proposed control theory frameworks to explain this, stochastic optimal control has become a dominant framework to the point of being a standard computational technique to reproduce kinematic trajectories of reaching movements (see) In particular, we demonstrate the application of optimal control to a neuromuscular model of the index finger with all seven musculotendons producing a tapping task. Our simulations include 1) a muscle model that includes force- length and force-velocity characteristics; 2) an anatomically plausible biomechanical model of the index finger that includes a tendinous network for the extensor mechanism and 3) a contact model that is based on a nonlinear spring-damper attached at the end effector of the index finger. We demonstrate that it is feasible to apply optimal control to systems with realistically large state vectors and conclude that, while optimal control is an adequate formalism to create computational models of neuro-musculoskeletal systems, there remain important challenges and limitations that need to be considered and overcome such as contact transitions, curse of dimensionality, and constraints on states and controls.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: Here, we promote the perspective that a computational model can be a rigorous crystallization of a hypothesis for the mechanisms generating observed data. We provide an example of using this approach to discriminate among hypotheses despite uncertainty in parameter values. Humans have been shown to produce non-uniform patterns of force fluctuation when they exert force in different directions with the index finger. We computationally formulated two hypotheses for this observation based on different cost functions of muscle effort, and then stochastically explored the space of unknown parameters to convergence to generate probability distributions of predictions from each hypothesis. The observed data were not within the probability distribution for Hypothesis 1: the sum of muscle forces is minimized, but were within the corresponding distribution for Hypothesis 2: the sum of squared muscle forces is minimized. Therefore, this approach provides rigorous evidence that Hypothesis 2 can not be rejected in favor of Hypothesis 1. The advantages and pitfalls of this computational approach to hypothesis testing are discussed.
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE; 10/2010
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ABSTRACT: Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data.
IEEE Reviews in Biomedical Engineering 02/2009;
