2024/01/09 by Liang Wu, Wu, Liang, Krystian Ganko +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Engineering · Medicine · #Advanced Control Systems Optimization #Cardiovascular Function and Risk Factors #Eicosanoids and Hypertension Pharmacology #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.04653
openalex publication_date 2024/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Determining solving-time certificates of nonlinear model predictive control (NMPC) implementations is a pressing requirement when deploying NMPC in production environments. Such a certificate guarantees that the NMPC controller returns a solution before the next sampling time. However, NMPC formulations produce nonlinear programs (NLPs) for which it is very difficult to derive their solving-time certificates. Our previous work, Wu and Braatz (2023), challenged this limitation with a proposed input-constrained MPC algorithm having exact iteration complexity but was restricted to linear MPC formulations. This work extends the algorithm to solve input-constrained NMPC problems, by using the Koopman operator and a condensing MPC technique. We illustrate the algorithm performance on a high-dimensional, nonlinear partial differential equation (PDE) control case study, in which we theoretically and numerically certify the solving time to be less than the sampling time.