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Time-average constraints in stochastic model predictive control

Time-average constraints in stochastic model predictive control
Time-average constraints in stochastic model predictive control
This paper presents two alternatives to using chance constraints in stochastic MPC, motivated by the observation that many stochastic constrained control algorithms aim to impose a bound on the time-average of constraint violations. We consider imposing a robust constraint on the time-average of constraint violations over a finite period. By allowing the controller to respond to the effects of past violations, two algorithms are presented that solve this problem, both requiring a single convex optimization after a preprocessing step. Stochastic MPC formulations that `remember' previous violations and react accordingly were given previously in [1], [2], but in those works the focus was on asymptotic guarantees on the average number of violations. In contrast we give stronger robust bounds on the violation permissible in any time period of a specified length. The method is also applied to a bound on the sum of convex loss functions of the amount of constraint violation, thus allowing controllers to place greater importance on avoiding large violations.
2378-5861
5648-5653
IEEE
Fleming, James
b59cb762-da45-43b1-b930-13dd9f26e148
Cannon, Mark
d2a52d25-9100-4a93-9bc7-8d10f4f3fa17
Fleming, James
b59cb762-da45-43b1-b930-13dd9f26e148
Cannon, Mark
d2a52d25-9100-4a93-9bc7-8d10f4f3fa17

Fleming, James and Cannon, Mark (2017) Time-average constraints in stochastic model predictive control. In 2017 American Control Conference (ACC). IEEE. pp. 5648-5653 . (doi:10.23919/ACC.2017.7963834).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper presents two alternatives to using chance constraints in stochastic MPC, motivated by the observation that many stochastic constrained control algorithms aim to impose a bound on the time-average of constraint violations. We consider imposing a robust constraint on the time-average of constraint violations over a finite period. By allowing the controller to respond to the effects of past violations, two algorithms are presented that solve this problem, both requiring a single convex optimization after a preprocessing step. Stochastic MPC formulations that `remember' previous violations and react accordingly were given previously in [1], [2], but in those works the focus was on asymptotic guarantees on the average number of violations. In contrast we give stronger robust bounds on the violation permissible in any time period of a specified length. The method is also applied to a bound on the sum of convex loss functions of the amount of constraint violation, thus allowing controllers to place greater importance on avoiding large violations.

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More information

Published date: 3 July 2017
Venue - Dates: 2017 American Control Conference, , Seattle, United States, 2017-05-23 - 2017-05-25

Identifiers

Local EPrints ID: 422577
URI: http://eprints.soton.ac.uk/id/eprint/422577
ISSN: 2378-5861
PURE UUID: 70a9554c-7f01-423f-9bfa-15076c899bbd
ORCID for James Fleming: ORCID iD orcid.org/0000-0003-2936-4644

Catalogue record

Date deposited: 25 Jul 2018 16:30
Last modified: 25 Nov 2021 20:17

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Contributors

Author: James Fleming ORCID iD
Author: Mark Cannon

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