Output feedback stochastic nonlinear model predictive control of a polymerization batch process

Abstract

Nonlinear model predictive control (NMPC) is one of the few methods that can handle multivariate nonlinear control problems while accounting for process constraints. Many dynamic models are however affected by significant stochastic uncertainties that can lead to closed-loop performance problems and infeasibility issues. In this paper we propose a novel stochastic NMPC (SNMPC) algorithm to optimize a probabilistic objective while adhering chance constraints for feasibility in which only noisy measurements are observed at each sampling time. The system predictions are assumed to be both affected by parametric and additive stochastic uncertainties. In particular, we use polynomial chaos expansions (PCE) to expand the random variables of the uncertainties. These are updated using a PCE nonlinear state estimator and exploited in the SNMPC formulation. The SNMPC scheme was verified on a complex polymerization semi-batch reactor case study.