Stability of model predictive control using Markov Chain Monte Carlo optimisation
Author(s)
Siva, E
Goulart, P
Maciejowski, JM
Kantas, N
Type
Conference Paper
Abstract
We apply stochastic Lyapunov theory to perform stability analysis of MPC controllers for nonlinear deterministic systems where the underlying optimisation algorithm is based on Markov Chain Monte Carlo (MCMC) or other stochastic methods. We provide a set of assumptions and conditions required for employing the approximate value function obtained as a stochastic Lyapunov function, thereby providing almost sure closed loop stability. We demonstrate convergence of the system state to a target set on an example, in which simulated annealing with finite time stopping is used to control a nonlinear system with non-convex constraints.
Date Issued
2009-08-23
Date Acceptance
2009-08-23
Citation
Proceedings of the 2009 European Control Conference (ECC), 2009, pp.2851-2856
ISBN
978-3-9524173-9-3
Publisher
IEEE
Start Page
2851
End Page
2856
Journal / Book Title
Proceedings of the 2009 European Control Conference (ECC)
Copyright Statement
© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://ieeexplore.ieee.org/document/7074840/
Source
2009 European Control Conference (ECC)
Publication Status
Published
Start Date
2009-08-23
Finish Date
2009-08-26
Coverage Spatial
Budapest, Hungary