利用能量函数观测值方法将Stratonovich框架转化为基于carleman滤波的马尔可夫卫星动力学

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS IFAC Journal of Systems and Control Pub Date : 2023-09-01 DOI:10.1016/j.ifacsc.2023.100224
Ravish H. Hirpara , Prashant G. Medewar
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引用次数: 0

摘要

本文分析了脉动气动力矩和轨道俯仰运动的径向摄动对卫星系统的影响。本文的目标有三个方面。第一个目标是“将卡尔曼线性化表示转化为马尔可夫过程的非线性随机演化”。二是在Carleman设定下对卫星动力学参数中的过程噪声和测量噪声进行滤波。三是找出卫星动力学的稳定性和收敛条件。第一个是通过统一生成函数、Carleman嵌入、Itô随机微分规则和Kronecker代数的有限闭包来实现的。关于第二个目标,我们通过Carleman嵌入将卫星动力学的有限维随机微分方程(SDE)重化为双线性随机微分方程的有限系统。第三个目标是获得涉及“Stratonovich微分”的卫星随机动力学的Lyapunov函数和渐近稳定条件。在本文中,我们通过“它的收敛性分析以及它与现有方法(即基准扩展卡尔曼滤波器、高斯二阶滤波器和Kushner-Stratonovich高阶滤波器)的优越性”来证明卫星动态滤波在基于卡尔曼滤波中的实用性。从仿真结果可以看出,基于carleman的滤波在卫星动态状态条件均值和条件方差的绝对滤波误差(AFE)方面优于其他基准滤波器。
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Stratonovich framework into Markovian satellite dynamics of Carleman-based filtering using energy function observables approach

In this paper the satellite system in which fluctuating aerodynamic torque and the radial perturbation about the pitch motion of orbit is analyzed. The objectives of the paper are three-fold. The first objective is the ‘Carleman linearization representation into the nonlinear stochastic evolution of the Markov process’. The second is to filter process and measurement noises in the satellite dynamics parameters in the Carleman setting. The third is to find the stability and convergence condition of satellite dynamics. The first is achieved by unifying the generating function, Carleman embedding, Itô stochastic differential rules and the finite closure with Kronecker algebra. Concerning the second objective, we recast the finite-dimensional Stochastic Differential Equations (SDE) of the satellite dynamics into a finite system of bilinear SDE via the Carleman embedding. The third objective is to achieve the Lyapunov function and asymptotic stability condition for the satellite stochastic dynamics involving the ‘Stratonovich differential’. In this paper, we demonstrate the utility of the satellite dynamics filtering in the Carleman-based filtering via ‘its convergence analysis as well as its superiority with available methods’, i.e., the benchmark extended Kalman filter, Gaussian second-order filter and Kushner–Stratonovich higher-order filter. From simulation performed it can be said that the Carleman-based filtering is superior than other benchmark filters in terms of their Absolute Filtering Error (AFE) of conditional means and conditional variances of the satellite dynamics states.

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来源期刊
IFAC Journal of Systems and Control
IFAC Journal of Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
3.70
自引率
5.30%
发文量
17
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