On optimal steering of a non-Markovian Gaussian process

被引：0

作者：

Alpago, Daniele ^{[1
]}

Chen, Yongxin ^{[2
]}

Georgiou, Tryphon ^{[3
]}

Pavon, Michele ^{[4
]}

机构：

[1] Univ Padua, Dipartimento Ingn Informaz, I-35131 Padua, Italy

[2] Georgia Inst Technol, Sch Aerosp Engn, Atlanta, GA 30332 USA

[3] Univ Calif Irvine, Dept Mech & Aerosp Engn, Irvine, CA 92697 USA

[4] Univ Padua, Dipartimento Matemat Tullio Levi Civita, I-35121 Padua, Italy

来源：

2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC) | 2019年

基金：

美国国家科学基金会;

关键词：

COVARIANCE CONTROL;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

At present, the problem to steer general non-Markovian processes between specified end-point marginal distributions with minimum energy remains unsolved. Herein, we consider the special case of a non-Markovian process y(t) which assumes a finite-dimensional stochastic realization with a Markov state process that is fully observable. In this setting, and over a finite time horizon [O, T], we determine an optimal (least) finite-energy control law that steers the stochastic system to a final distribution that is compatible with a specified distribution for the terminal output process y(T); the solution is given in closed-form. This work provides a key step towards the important problem to steer a stochastic system based on partial observations of the state (i.e., an output process) corrupted by noise.

引用

页码：2556 / 2561

页数：6

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