Four MPC implementations compared on the Quadruple Tank Process Benchmark: pros and cons of neural MPC*. Issue 16 (2022)

• Record Type: Journal Article
• Authors: Blaud, Pierre Clément; Chevrel, Philippe; Claveau, Fabien; Haurant, Pierrick; Mouraud, Anthony
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 16(2022)
• Page Start: 344
• Page End: 349
• Publication Date: 2022
• Subjects: Model predictive control -- artificial neural network -- quadruple tank process; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.09.048
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This study aims to aid understanding of Model Predictive Control (MPC) alternatives through comparing most interesting MPC implementations. This comparison will be performed intrinsically and illustrated using the four-tank benchmark, widely studied by academics taking care of industrial perspectives. Although MPC provides advanced control solutions for a wide class of dynamical systems, challenges arise in managing the compromise between accuracy, computational cost and resilience, depending on the type of model used. In this study, linear, linear time-varying and non-linear MPCs are compared to MPC that uses a neural network based predictive model identified from data. The tuning and implementation methods considered are discussed, and accurate simulation results provided and analyzed. Precisely, the performance of each method (linear, linear time-varying, non-linear MPC) are compared to the neural MPC. Pros and cons of neural MPC are highlighted.