Stochastic model predictive control with joint chance constraints
This article investigates model predictive control (MPC) of linear systems subject to arbitrary (possibly unbounded) stochastic disturbances. An MPC approach is presented to account for hard input constraints and joint state chance constraints in the presence of unbounded additive disturbances. The...
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Published in | International journal of control Vol. 93; no. 1; pp. 126 - 139 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Abingdon
Taylor & Francis
02.01.2020
Taylor & Francis Ltd |
Subjects | |
Online Access | Get full text |
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Summary: | This article investigates model predictive control (MPC) of linear systems subject to arbitrary (possibly unbounded) stochastic disturbances. An MPC approach is presented to account for hard input constraints and joint state chance constraints in the presence of unbounded additive disturbances. The Cantelli-Chebyshev inequality is used in combination with risk allocation to obtain computationally tractable but accurate surrogates for the joint state chance constraints when only the mean and variance of the arbitrary disturbance distributions are known. An algorithm is presented for determining the optimal feedback gain and optimal risk allocation by iteratively solving a series of convex programs. The proposed stochastic MPC approach is demonstrated on a continuous acetone-butanol-ethanol fermentation process, which is used in the production of biofuels. |
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ISSN: | 0020-7179 1366-5820 |
DOI: | 10.1080/00207179.2017.1323351 |