标题： 基于层级的逆随机最优控制 显示英文标题

标题： Bi-Level-Based Inverse Stochastic Optimal Control

摘要： 在本文中，我们提出了一种新算法来解决线性二次感觉运动（LQS）控制模型的逆随机最优控制（ISOC）问题。LQS模型代表了目前描述目标导向人类运动的最先进技术。ISOC问题旨在从测量数据中确定LQS模型的成本函数和噪声缩放矩阵，因为这两种参数都会影响模型预测的统计矩，并且在实际中是未知的。我们证明了我们新算法的全局收敛性，并通过一个数值例子验证了我们方法的理论假设。通过全面的仿真，分析了我们算法的调节参数对收敛行为和计算时间的影响。新算法计算ISOC解的速度比之前唯一存在的ISOC算法快近33倍。 显示英文摘要

摘要： In this paper, we propose a new algorithm to solve the Inverse Stochastic Optimal Control (ISOC) problem of the linear-quadratic sensorimotor (LQS) control model. The LQS model represents the current state-of-the-art in describing goal-directed human movements. The ISOC problem aims at determining the cost function and noise scaling matrices of the LQS model from measurement data since both parameter types influence the statistical moments predicted by the model and are unknown in practice. We prove global convergence for our new algorithm and at a numerical example, validate the theoretical assumptions of our method. By comprehensive simulations, the influence of the tuning parameters of our algorithm on convergence behavior and computation time is analyzed. The new algorithm computes ISOC solutions nearly 33 times faster than the single previously existing ISOC algorithm.