Optimal State Estimation for Discrete-Time Markov Jump Systems with Missing Observations

This paper is concerned with the optimal linear estimation for a class of direct-time Markov jump systems with missing observations. An observer-based approach of fault detection and isolation (FDI) is investigated as a detection mechanic of fault case. For systems with known information, a conditional prediction of observations is applied and fault observations are replaced and isolated; then, an FDI linear minimum mean square error estimation (LMMSE) can be developed by comprehensive utilizing of the correct information offered by systems. A recursive equation of filtering based on the geometric arguments can be obtained. Meanwhile, a stability of the state estimator will be guaranteed under appropriate assumption

Robust finite-horizon Kalman filtering for uncertain discrete-time systems

In this note, we propose a design for a robust finite-horizon Kalman filtering for discrete-time systems suffering from uncertainties in the modeling parameters and uncertainties in the observations process (missing measurements). The system parameter uncertainties are expected in the state, output and white noise covariance matrices. We find the upper-bound on the estimation error covariance and we minimize the proposed upper-bound

モーションコントロールへの応用のためのカルマンフィルタに関する研究 : デュアルレート・時間遅延補償・パラメータ推定

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 堀 洋一, 東京大学教授 大崎 博之, 東京大学教授 古関 隆章, 東京大学教授 久保田 孝, 東京大学客員准教授 坂井 真一郎, 東京大学准教授 藤本 博志University of Tokyo(東京大学