2024-08-052024-08-052023-11-032023 XX Workshop on Information Processing and Control (RPIC)978-950-766-230-0http://hdl.handle.net/20.500.12272/11241Most industrial processes are nonlinear, which complicates the application of conventional Model-based Predictive Control (MPC) algorithms. Consequently, in this article, the formulations of MPC methods for nonlinear processes represented through polytopic Linear Parameter-Varying models are analysed. The compared methods are adaptive algorithm, synthesised with a prediction model based on a scheduling polytope. At each discrete sampling instant, they determine a model, used for prediction purposes; and optimise the process performances over a finite prediction horizon. These methods are applied to control of a Heat Exchanger system, from which the performance and effectiveness of each technique are discussed. The simulation results are thoroughly analyzed, and the advantages and disadvantages of each strategy are discussed.plainengopenAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 InternacionalModel-based predictive controlLinear parameter-varyingNonlinear systemHeat exchangerinfo:eu-repo/semantics/conferenceObject.