Development of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration
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Chemical Engineering Communications
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FRANCO, Ivan Carlos; SCHMITZ, J. E.; COSTA, T. V.; FILETI, A. M. F.; SILVA, F. V.. Development of a Predictive Control Based on Takagi-Sugeno Model Applied in a Nonlinear System of Industrial Refrigeration. Chemical Engineering Communications (Print), v. 204, n. 1, p. 39-54, 2017.
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© 2017, Copyright © Taylor & Francis Group, LLC.Refrigeration systems exist in different branches of industry and are characterized as great energy consumers with considerable nonlinear behavior. Several studies have promoted energy costs reduction and minimization of nonlinearities effects in such systems. Model predictive control has been successfully used to stabilize processes in the presence of such nonlinearities; therefore, its application in refrigeration systems is considered promising. In the present study, Takagi–Sugeno models were developed and validated in order to predict the evaporating and secondary fluid temperatures (TE and TP) based on the ANFIS technique (Adaptive Network-based Fuzzy Inference Systems) for a vapor-compressor chiller equipment. The prediction performance of resulting models was analyzed and accessed based on the variance accounted for criteria. These models were then used as the basis for prediction models in several generalized predictive controllers (GPC) denoted here as GPC-ANFIS controllers. Different predictive controllers were designed for different local rules (Fuzzy rules) and the global control action was assumed as the weighted sum of local controllers. Experimental tests considered two distinct controllers, namely the GPC-ANFISTE (evaporating temperature control by means of compressor speed variation) and GPC-ANFISTP (propylene glycol temperature control by means of compressor speed variation), were performed. The experimental tests for setpoint tracking (±1°C) considering 3000 W of constant heat load showed satisfactory results with setpoint deviation around ±0.3°C. Therefore, the ANFIS technique demonstrated to be able to provide reliable predictive models to be used in generalized predictive control algorithms.