Model Predictive Control of Stochastic Linear Systems with Probability Constraints


This paper presents a strategy for computing model predictive control of linear Gaussian noise systems with probability constraints. As usual, constraints are taken on the system state and control input. The novelty relies on setting bounds on the underlying cumulative probability distribution, and showing that the model predictive control can be computed in an efficient manner through these novel bounds— an application confirms this assertion. Indeed real-time experiments were carried out to control a direct current (DC) motor. The corresponding data show the effectiveness and usefulness of the approach.


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How to Cite
CARUNTU, Constantin F. et al. Model Predictive Control of Stochastic Linear Systems with Probability Constraints. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 6, p. 927-937, nov. 2018. ISSN 1841-9844. Available at: <>. Date accessed: 16 july 2020. doi:


probability constraints, stochastic systems, linear systems, control