Scenario-Based Model Predictive Control for Systems with Correlated Uncertainties

Abstract

The vast majority of real-world processes have inherent uncertainties, which, when considered in the modelling process, can provide a representation that most accurately describes the behaviour of the real process. Such uncertainties may be parametric, due to the nature of the process, and/or additive, due to external disturbances. In most practical cases, these are considered to have stochastic behaviour and their descriptions as probability distributions are known.Stochastic model predictive control algorithms are developed to control processes with uncertainties of a stochastic nature, where the knowledge of the statistical properties of the uncertainties is exploited by including it in the optimal control problem (OCP) statement. Contrary to other model predictive control (MPC) schemes, hard constraints are relaxed by reformulating them as probabilistic constraints to reduce conservatism. That is, violations of the original hard constraints are allowed, but such violations must not exceed a permitted level of risk. BESS