论文标题
自由能原理，用于具有彩色噪声的线性系统的噪声平滑度估算
Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise
论文作者
论文摘要
神经科学的自由能原理（FEP）提供了一个称为主动估计和控制状态空间系统的框架，并受到彩色噪声的影响。但是，主动推论社区受到手动调整噪声平滑参数的关键任务的挑战。为了解决这个问题，我们根据自由能原理的想法引入了一种新颖的在线噪声平滑度估计器。我们从数学上表明，在平滑度估计过程中，我们的估计器可以收敛到自由能的最佳最佳。使用此公式，我们引入了称为DEMS的联合状态和噪声平滑度观察者设计。通过严格的模拟，我们表明DEMS的表现优于最先进的状态观察者，而状态估计误差最少。最后，我们通过将DEM应用于现实生活机器人问题问题 - 状态估算四四旋转盘盘悬停在风中，为DEM提供了概念证明，证明了其实际使用。
The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise. However, the active inference community has been challenged with the critical task of manually tuning the noise smoothness parameter. To solve this problem, we introduce a novel online noise smoothness estimator based on the idea of free energy principle. We mathematically show that our estimator can converge to the free energy optimum during smoothness estimation. Using this formulation, we introduce a joint state and noise smoothness observer design called DEMs. Through rigorous simulations, we show that DEMs outperforms state-of-the-art state observers with least state estimation error. Finally, we provide a proof of concept for DEMs by applying it on a real life robotics problem - state estimation of a quadrotor hovering in wind, demonstrating its practical use.
