%0 Journal Article
%K Model predictive control (MPC)
%K Building HVAC optimal control
%K Optimal control problem formulations comparison
%K Mixed integer nonlinear optimization
%A Ettore Zanetti
%A Donghun Kim
%A David Blum
%A Rossano Scroccia
%A Marcello Aprile
%B Journal of Building Performance Simulation
%D 2022
%G eng
%P 1 - 19
%R 10.1080/19401493.2022.2120631
%T Performance comparison of quadratic, nonlinear, and mixed integer nonlinear MPC formulations and solvers on an air source heat pump hydronic floor heating system
%U https://www.tandfonline.com/doi/full/10.1080/19401493.2022.2120631
%8 09/2022
%! Journal of Building Performance Simulation
%X <p>There is a gap in literature on comparisons between different MPC optimal control formulations</p><p>and solver choices for the same building HVAC system. Mixed Integer Nonlinear (MINL) formu-<br />lations are rarely considered, despite being the most physically accurate way to represent HVAC</p><p>systems. This work compares several MPC formulations, including Quadratic, Nonlinear, and MINL,</p><p>applied to a case study building and investigates benefits and challenges of MINL MPCs from prac-<br />tical perspectives. Ten different MPC formulations were developed and implemented using Pyomo.</p><p>Then, a detailed emulator model was developed using open-source Modelica libraries and used<br />with BOPTEST to assess the performance of each MPC. Results show that convergence and control<br />switching behaviours of MINL MPCs are sensitive to formulations, initialization approaches, solver<br />selections, and solver parameters. Thus, they require significant effort for tuning. However, a very<br />well-tuned MINL MPC performed similarly to successful Nonlinear MPC formulations.</p>