Tractable robust MPC design based on nominal predictions

Ignacio Alvarado; Pablo Krupa; Daniel Limon; Teodoro Alamo

doi:10.1016/j.jprocont.2022.01.006

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arXiv · 2104.06088

Tractable robust MPC design based on nominal predictions

Ignacio Alvarado·Pablo Krupa·Daniel Limon·Teodoro Alamo

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Abstract

Many popular approaches in the field of robust model predictive control (MPC) are based on nominal predictions. This paper presents a novel formulation of this class of controller with proven input-to-state stability and robust constraint satisfaction. Its advantages are: (i) the design of its main ingredients are tractable for medium to large-sized systems, (ii) the terminal set does not need to be robust with respect to all the possible system uncertainties, but only for a reduced set that can be made arbitrarily small, thus facilitating its design and implementation, (iii) under certain conditions the terminal set can be taken as a positive invariant set of the nominal system, allowing us to use a terminal equality constraint, which facilitates its application to large-scale systems, and (iv) the complexity of its optimization problem is comparable to the non-robust MPC variant. We show numerical closed-loop results of its application to a multivariable chemical plant and compare it against other robust MPC formulations.

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Ignacio Alvarado, Pablo Krupa, Daniel Limon, Teodoro Alamo. 2022-02-19. Tractable robust MPC design based on nominal predictions. https://doi.org/10.1016/j.jprocont.2022.01.006

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