Noome, Zander M.Le Roux, Johan Derik2022-11-232022-11-232022Noome, Z.M. & Le Roux, J.D. 2022, 'Controlling a grinding mill circuit using constrained model predictive static programming', IFAC-PapersOnLine, vol. 55, no. 21, pp. 49-54, doi: 10.1016/j.ifacol.2022.09.242.2405-8963 (online)10.1016/j.ifacol.2022.09.242https://repository.up.ac.za/handle/2263/88453A constrained Model Predictive Static Programming (MPSP) method is implemented in simulation to a single-stage grinding mill circuit model. The results are compared to a constrained Nonlinear Model Predictive Control (NMPC) method. Both the constrained MPSP and NMPC controllers were able to track the desired output set-points without exceeding any constraints. The comparison shows that the constrained MPSP has a faster computational time than that of the NMPC controller with similar performance. Therefore, constrained MPSP shows promise as a model-based controller for large processes where computational time limits the use of NMPC.en© 2022 The Authors. This is an open access article under the CC BY-NC-ND license.Computational timeGrinding millIndustrial processesModel predictive static programming (MPSP)Nonlinear model predictive control (NMPC)Article