Abstract
Stochastic uncertainty is present in many control engineering problems, and is also present in a wider class of applications, such as finance and sustainable development. We propose a receding horizon strategy for systems with multiplicative stochastic uncertainty in the dynamic map between plant inputs and outputs. The cost and constraints are defined using probabilistic bounds. Terminal constraints are defined in a probabilistic framework, and guarantees of closed-loop convergence and recursive feasibility of the online optimization problem are obtained. The proposed strategy is compared with alternative problem formulations in simulation examples.
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Cannon, 1., Couchman, P., Kouvaritakis, B. (2007). MPC for Stochastic Systems. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_20
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DOI: https://doi.org/10.1007/978-3-540-72699-9_20
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