Stochastic MPC for additive and multiplicative uncertainty using sample approximations

Journal article

Abstract:: We introduce an approach for Model Predictive Control (MPC) of systems with additive and multiplicative stochastic uncertainty subject to chance constraints. Predicted states are bounded within a tube and the chance constraint is considered in a 'one step ahead' manner, with robust constraints applied over the remainder of the horizon. The online optimization is formulated as a chance-constrained program which is solved approximately using sampling. We prove that if the optimization is initially feasible, it remains feasible and the closed-loop system is stable. Applying the chance-constraint only one step ahead allows us to state a confidence bound for satisfaction of the chance constraint in closed-loop. Finally, we demonstrate by example that the resulting controller is only mildly more conservative than scenario MPC approaches that have no feasibility guarantee.

Files:: paper.pdf

(Preview, Accepted manuscript, pdf, 503.6KB, Terms of use)

Keywords:: stochastic processes

uncertainty

optimization

electron tubes

additives

uncertain systems

predictive control
Pubs id:: pubs:951230
UUID:: uuid:04fb0168-6a60-43ed-b282-18d961d1d92a
Local pid:: pubs:951230
Source identifiers:: 951230
Deposit date:: 2018-12-08

Copyright holder:: Institute of Electrical and Electronics Engineers
Notes:: © 2018 IEEE. This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/TAC.2018.2887054

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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