Stochastic receding horizon control with output feedback and bounded control inputs

被引：9

作者：

Hokayem, Peter ^{[1
]}

Cinquemani, Eugenio ^{[2
]}

Chatterjee, Debasish ^{[1
]}

Ramponi, Federico ^{[1
]}

Lygeros, John ^{[1
]}

机构：

[1] ETH, Automat Control Lab, Phys Str 3, CH-8092 Zurich, Switzerland

[2] INRIA, Grenoble, France

来源：

49TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) | 2010年

基金：

瑞士国家科学基金会;

关键词：

MODEL-PREDICTIVE CONTROL; ROBUST; OPTIMIZATION; STABILITY; POLICIES; SYSTEMS; STATE;

D O I：

10.1109/CDC.2010.5717990

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We study the problem of receding horizon control of stochastic discrete-time systems with bounded control inputs and incomplete state information. Given a suitable choice of causal control policies, we first present a slight extension of the Kalman filter to estimate the state optimally in mean-square sense. We then show how to augment the underlying optimization problem with a negative drift-like constraint, yielding a second-order cone program to be solved periodically online. Finally, we prove that the receding horizon implementation of the resulting control policies renders the state of the overall system mean-square bounded under mild assumptions.

引用

页码：6095 / 6100

页数：6

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