论文标题
部分可观测时空混沌系统的无模型预测
Inverse Stochastic Optimal Control for Linear-Quadratic Gaussian and Linear-Quadratic Sensorimotor Control Models
论文作者
论文摘要
在本文中，我们定义并解决了线性季度高斯（LQG）和线性季度感觉运动模型（LQS）控制模型的逆随机最佳控制（ISOC）问题。这些随机最佳控制（SOC）模型是描述人类运动的最先进方法。 LQG ISOC问题包括找到二次成本函数的未知加权矩阵以及基于实际上从人类在实践中观察到的地面真实轨迹的加性高斯噪声过程的协方差矩阵。 LQS ISOC问题旨在另外找到LQS模型信号依赖性噪声过程的协方差矩阵。我们提出了两个ISOC问题的解决方案，该解决方案通过两个双层优化迭代估算成本函数和协方差矩阵。仿真示例显示了我们开发的算法的有效性。它发现参数可以产生与地面真相数据的均值和方差相匹配的轨迹。
In this paper, we define and solve the Inverse Stochastic Optimal Control (ISOC) problem of the linear-quadratic Gaussian (LQG) and the linear-quadratic sensorimotor (LQS) control model. These Stochastic Optimal Control (SOC) models are state-of-the-art approaches describing human movements. The LQG ISOC problem consists of finding the unknown weighting matrices of the quadratic cost function and the covariance matrices of the additive Gaussian noise processes based on ground truth trajectories observed from the human in practice. The LQS ISOC problem aims at additionally finding the covariance matrices of the signal-dependent noise processes characteristic for the LQS model. We propose a solution to both ISOC problems which iteratively estimates cost function and covariance matrices via two bi-level optimizations. Simulation examples show the effectiveness of our developed algorithm. It finds parameters that yield trajectories matching mean and variance of the ground truth data.
