Stochastic receding horizon control with output feedback and bounded controls

被引:85
|
作者
Hokayem, Peter [1 ]
Cinquemani, Eugenio [2 ]
Chatterjee, Debasish [3 ]
Ramponi, Federico [4 ]
Lygeros, John [1 ]
机构
[1] ETH, Automat Control Lab, CH-8092 Zurich, Switzerland
[2] INRIA Grenoble Rhone Alpes, F-38334 Montbonnot St Martin, St Ismier, France
[3] IIT, Bombay 400076, Maharashtra, India
[4] Univ Brescia, Dept Informat Engn, I-25123 Brescia, Italy
基金
瑞士国家科学基金会;
关键词
Predictive control; Output feedback; Constrained control; State estimation; Stochastic control; OPTIMIZATION; STABILITY; POLICIES; SYSTEMS; STATE;
D O I
10.1016/j.automatica.2011.09.048
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 for 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. 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. We also discuss how some quantities required by the finite-horizon optimization problem can be computed off-line, thus reducing the on-line computation. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:77 / 88
页数:12
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