Predictive algorithms for neuromuscular control of human locomotion
Predictive algorithms for neuromuscular control of human locomotion
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DOI：
10.1016/s0021-9290(01)00057-4
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发表时间：
2001-08-01
影响因子：
2.4
通讯作者：
Heegaard, JH
中科院分区：
文献类型：
--
作者：
Kaplan, ML;Heegaard, JH
量化人体肌肉活动的问题可以表述为最优控制问题。当前用于大规模生物力学系统的方法是非衍生技术。这些方法成本高昂，因为它们需要对运动方程进行多次积分。此外，收敛速度很慢，使得它们不适用于大型系统。我们将有效的数值算法应用于生物力学最优控制问题。使用梯形离散化的直接搭配，将运动方程转换为一组代数约束方程。优化问题使用增强拉格朗日公式来处理等式和不等式约束。由此产生的最小最大问题可以用广义牛顿法来解决。与流行的最优控制实现相反，我们计算分析一阶和二阶导数信息并获得局部二次收敛。为了证明该方法的有效性，我们解决了具有 7 个节段和 18 个独立肌肉群的稳态踩踏问题。计算出的肌肉激活与实验肌电图数据非常吻合。计算量显着减少，求解时间也只是非导数技术的一小部分。 (C) 2001 Elsevier Science Ltd. 保留所有权利。
The problem of quantifying muscular activity of the human body can be formulated as an optimal control problem. The current methods used with large-scale biomechanical systems are non-derivative techniques. These methods are costly, as they require numerous integrations of the equations of motion. Additionally, the convergence is slow, making them impractical for use with large systems. We apply an efficient numerical algorithm to the biomechanical optimal control problem. Using direct collocation with a trapezoidal discretization, the equations of motion are converted into a set of algebraic constraint equations. An augmented Lagrangian formulation is used for the optimization problem to handle both equality and inequality constraints. The resulting minmax problem is solved with a generalized Newton method. In contrast to the prevalent optimal control implementations, we calculate analytical first- and second-derivative information and obtain local quadratic convergence. To demonstrate the efficacy of the method, we solve a steady-state pedaling problem with 7 segments and 18 independent muscle groups. The computed muscle activations compare well with experimental EMG data. The computational effort is significantly reduced and solution times are a fraction of those of the non-derivative techniques. (C) 2001 Elsevier Science Ltd. All rights reserved.