Stochastic receding horizon control with output feedback and bounded controls |
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Authors: | Peter Hokayem Eugenio Cinquemani Debasish Chatterjee Federico Ramponi John Lygeros |
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Affiliation: | 1. Automatic Control Laboratory, ETH Zürich, Physikstrasse 3, 8092 Zürich, Switzerland;2. INRIA Grenoble-Rhône-Alpes, 655 avenue de l’Europe, Montbonnot, 38 334 Saint Ismier Cedex, France;3. Systems & Control Engineering, IIT Bombay, Powai, Mumbai 400076, India;4. Department of Information Engineering, Università degli Studi di Brescia, via Branze 38, 25123 Brescia, Italy |
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Abstract: | 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. |
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