Stochastic output-feedback model predictive control

Stochastic output-feedback model predictive control A new formulation of Stochastic Model Predictive Output Feedback Control is presented and analyzed as a transposition of Stochastic Optimal Output Feedback Control into a receding horizon setting. This requires lifting the design into a framework involving propagation of the conditional state density, the information state, and solution of the Stochastic Dynamic Programming Equation for an optimal feedback policy, both stages of which are computationally challenging in the general, nonlinear setup. The upside is that the clearance of three bottleneck aspects of Model Predictive Control is connate to the optimality: output feedback is incorporated naturally; dual regulation and probing of the control signal is inherent; closed-loop performance relative to infinite-horizon optimal control is guaranteed. While the methods are numerically formidable, our aim is to develop an approach to Stochastic Model Predictive Control with guarantees and, from there, to seek a less onerous approximation. To this end, we discuss in particular the class of Partially Observable Markov Decision Processes, to which our results extend seamlessly, and demonstrate applicability with an example in healthcare decision making, where duality and associated optimality in the control signal are required for satisfactory closed-loop behavior. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatica Elsevier

Stochastic output-feedback model predictive control

Loading next page...
 
/lp/elsevier/stochastic-output-feedback-model-predictive-control-Y1DG5E3eOX
Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0005-1098
D.O.I.
10.1016/j.automatica.2018.04.013
Publisher site
See Article on Publisher Site

Abstract

A new formulation of Stochastic Model Predictive Output Feedback Control is presented and analyzed as a transposition of Stochastic Optimal Output Feedback Control into a receding horizon setting. This requires lifting the design into a framework involving propagation of the conditional state density, the information state, and solution of the Stochastic Dynamic Programming Equation for an optimal feedback policy, both stages of which are computationally challenging in the general, nonlinear setup. The upside is that the clearance of three bottleneck aspects of Model Predictive Control is connate to the optimality: output feedback is incorporated naturally; dual regulation and probing of the control signal is inherent; closed-loop performance relative to infinite-horizon optimal control is guaranteed. While the methods are numerically formidable, our aim is to develop an approach to Stochastic Model Predictive Control with guarantees and, from there, to seek a less onerous approximation. To this end, we discuss in particular the class of Partially Observable Markov Decision Processes, to which our results extend seamlessly, and demonstrate applicability with an example in healthcare decision making, where duality and associated optimality in the control signal are required for satisfactory closed-loop behavior.

Journal

AutomaticaElsevier

Published: Aug 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off