Theodorou Evangelos, Valero-Cuevas Francisco J
Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4510-6. doi: 10.1109/IEMBS.2010.5626055.
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 [12]) 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.
我们概述了用于非线性神经肌肉系统的最优控制方法，并讨论了它们的局限性。此外，我们将当前的最优控制方法扩展到其在具有实际大量肌腱的神经肌肉模型中的应用；因为大多数先前的工作仅限于扭矩驱动系统。最近关于计算运动控制的工作探索了将控制理论和估计作为一种概念工具，以理解神经肌肉系统的潜在计算原理。毕竟，成功的生物系统经常满足多种复杂任务的稳定性、鲁棒性和性能条件。在各种提出的用于解释这一点的控制理论框架中，随机最优控制已成为一个主导框架，以至于成为一种用于再现伸手运动运动轨迹的标准计算技术（见[12]）。特别是，我们展示了最优控制在食指神经肌肉模型中的应用，该模型的所有七条肌腱都能执行敲击任务。我们的模拟包括：1）一个包含力-长度和力-速度特性的肌肉模型；2）一个解剖学上合理的食指生物力学模型，该模型包括伸肌机制的腱网络；3）一个基于附着在食指末端效应器上的非线性弹簧-阻尼器的接触模型。我们证明了将最优控制应用于具有实际大状态向量的系统是可行的，并得出结论，虽然最优控制是创建神经肌肉骨骼系统计算模型的一种适当形式，但仍存在需要考虑和克服的重要挑战和局限性，如接触转换、维度诅咒以及对状态和控制的约束。
