Premium
Stochastic model predictive control: Insights and performance comparisons for linear systems
Author(s) -
Seron Maria M.,
Goodwin Graham C.,
Carrasco Diego S.
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4106
Subject(s) - model predictive control , computation , computer science , control (management) , stochastic control , mathematical optimization , linear system , control theory (sociology) , mathematics , optimal control , algorithm , artificial intelligence , mathematical analysis
Summary In this paper, we define several instances of model predictive control (MPC) for linear systems, including both deterministic and stochastic formulations. We show by explicit computation of the associated control laws that, under certain conditions, different formulations lead to identical results. This paper provides insights into the performance of stochastic MPC. Amongst other things, it shows that stochastic MPC and traditional MPC can give identical results in special cases. In cases where the solutions are different, we show that the explicit formulation of the problem can give insight into the performance gap.