Advanced-step multistage nonlinear model predictive control: Robustness and stability. (January 2020)

• Record Type: Journal Article
• Authors: (Joyce) Yu, Zhou; Biegler, Lorenz T.
• Is Part Of: Journal of process control. Volume 85(2020)
• Page Start: 15
• Page End: 29
• Publication Date: 2020-01
• Subjects: Robust nonlinear model predictive control -- Real-time -- Optimal control -- Dynamic optimization -- Stochastic programming -- Sensitivity; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.10.005
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Multi-stage Nonlinear MPC (msNMPC) extended to sensitivity-based NLP concepts. Advanced step msNMPC (as-msNMPC) control strategy developed and implemented. Both msNMPC and as-msNMPC analyzed with nominal and ISS Lyapunov stability proved. msNMPC, as-NMPC compared three other robust control strategies on two case studies. as-msNMPC leads to robust MPC and requires significantly less on-line computation. Abstract: Nonlinear model predictive control (NMPC) has been popular in many applications, especially when constraint satisfaction is critical. However, due to plant-model mismatch and disturbances, robust NMPC generally faces three challenges: robust performance, real-time implementation, and stability. In this paper, we propose a parallelizable advanced-step multistage NMPC (as-msNMPC), which provides a non-conservative robust control solution that explicitly addresses two types of uncertainty: model parameters and unmeasured noise. The first type is attended to by incorporating scenario trees and the second by applying nonlinear programming (NLP) sensitivity. In addition, robust stability concepts have been discussed for both ideal multistage NMPC (ideal-msNMPC) and as-msNMPC. Under suitable assumptions, as-msNMPC has demonstrated input-to-state practical stability properties with the presence of two types of uncertainty. Lastly, the as-msNMPC framework has been applied to continuous stirred tank reactor (CSTR) and quad-tank case studies for tracking setpoints to demonstrate its performance in robustness and efficiency.