Controle preditivo baseado em modelo neuro-fuzzy de sistemas não-lineares aplicado em sistema de refrigeração = : Model predictive control based on neuro-fuzzy nonlinear systems applied to a refrigeration plant / Model predictive control based on neuro-fuzzy nonlinear systems applied to a refrigeration plant

Ivan Carlos Franco

Refrigeration systems can be found in many different branches of industry and are characterized as great energy consumers with considerable non-linear behavior. Several studies have been developed to promote the reduction of energy costs and to minimize the effects of nonlinearities in these systems. The use of automation and process control, particularly the use of advanced control techniques, is a widely used strategy for this purpose. The Model Predictive Control (MPC) is capable of stabilizing processes in which there are nonlinearities, and it is a promising application in refrigeration systems. In this work, a System for Monitoring and Advanced Control in Refrigeration (SISMOCAR) was developed using OPC (OLE for Process Control) communication. This feature allowed beyond real time monitoring for all variables involved in the refrigeration cycle, the control of the relevant variables. Furthermore, to predict the evaporating (Te) and the secondary fluid (Tp) temperatures, Takagi-Sugeno models (SISO) were developed and validated using the ANFIS (Adaptive Network-based Fuzzy Inference Systems) technique, with performance analysis based on the VAF (Variance accounted for) calculation. The validated Takagi-Sugeno models were used as basis for Predictive Controllers, specifically using Generalized Predictive Controller (GPC) strategy. The GPC controllers were developed without constraints in the objective function of the controller action. Different predictive controllers were designed for different local rules (Fuzzy Rules), being the weighted integration of the local models the controller global action. Three different controllers were developed: GPC1 (evaporating temperature control using the Te predictive model as a function of compressor frequency); GPC2 (control of propylene glycol temperature using the Tp predictive model as a function of compressor frequency) and GPC3 (control of propylene glycol temperature using the Tp predictive model as a function of the frequency of the evaporator pump). The tests performed for the set-point tracking (± 1 °C), with constant thermal load of 3000 W, were considered satisfactory and best performances were those obtained by GPC1 and GPC2 controllers, in which the controlled variable was around ± 0,3 °C.

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