Stochastic model predictive control — how does it work?. (9th June 2018)

• Record Type: Journal Article
• Authors: Heirung, Tor Aksel N.; Paulson, Joel A.; O'Leary, Jared; Mesbah, Ali
• Is Part Of: Computers & chemical engineering. Volume 114(2018)
• Page Start: 158
• Page End: 170
• Publication Date: 2018-06-09
• Subjects: Model predictive control -- Stochastic optimal control -- Chance constraints -- Disturbance modeling; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.10.026
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Providing a tutorial overview of SMPC. Illustrating the relation between SMPC and classical optimal control as well as nominal MPC. Highlighting the importance of stochastic disturbance modeling in the context of SMPC. Abstract: Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance constraints, which enables a systematic trade-off between attainable control performance and probability of state constraint violations in a stochastic setting. This paper presents an overview of core concepts in SMPC in relation to MPC and stochastic optimal control, with numerical illustrations on a typical chemical process. Estimation of stochastic disturbances as well as the impact of estimation quality of stochastic disturbances on the SMPC performance are discussed. Some avenues for future research in SMPC are suggested.