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Shrinking Horizon Model Predictive Control With Signal Temporal Logic Constraints Under Stochastic Disturbances

Lookup NU author(s): Dr Sadegh SoudjaniORCiD

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2019.

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Abstract

We present shrinking horizon model predictive control for discrete-time linear systems under stochastic disturbances with constraints encoded as signal temporal logic (STL) specification. The control objective is to satisfy a given STL specification with high probability against stochastic uncertainties while maximizing the robust satisfaction of an STL specification with minimum control effort. We formulate a general solution, which does not require precise knowledge of probability distributions of (possibly dependent) stochastic disturbances; only the bounded support of the density functions and moment intervals are used. For the specific case of disturbances that are normally distributed, we optimize the controllers by utilizing knowledge of the probability distribution of the disturbance. We show that in both cases, the control law can be obtained by solving optimization problems with linear constraints at each step. We experimentally demonstrate effectiveness of this approach by synthesizing a controller for a heating, ventilation, and air conditioning system.

Publication metadata

Author(s): Farahani SS, Majumdar R, Prabhu VS, Soudjani S

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Automatic Control

Year: 2019

Volume: 64

Issue: 8

Pages: 3324-3331

Print publication date: 01/08/2019

Online publication date: 09/11/2018

Acceptance date: 11/10/2018

Date deposited: 04/11/2019

ISSN (print): 0018-9286

ISSN (electronic): 1558-2523

Publisher: IEEE

URL: https://doi.org/10.1109/TAC.2018.2880651

DOI: 10.1109/TAC.2018.2880651

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