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Fuzzy modelbased predictive control using TakagiSugeno models
, 1999
"... Nonlinear modelbased predictive control (MBPC) in multiinput multioutput (MIMO) process control is attractive for industry. However, two main problems need to be considered: (i) obtaining a good nonlinear model of the process, and (ii) applying the model for control purposes. In this paper, recen ..."
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Nonlinear modelbased predictive control (MBPC) in multiinput multioutput (MIMO) process control is attractive for industry. However, two main problems need to be considered: (i) obtaining a good nonlinear model of the process, and (ii) applying the model for control purposes. In this paper, recent work focusing on the use of TakagiSugeno fuzzy models in combination with MBPC is described. First, the fuzzy modelidentification of MIMO processes is given. The process model is derived from inputoutput data by means of productspace fuzzy clustering. The MIMO model is represented as a set of coupled multiinput, singleoutput (MISO) models. Next, the TakagiSugeno fuzzy model is used in combination with MBPC. The critical element in nonlinear MBPC is the optimization routine which is nonconvex and thus difficult to solve. Two methods to deal with this problem are developed: (i) a branchandbound method with iterative gridsize reduction, and (ii) control based on a local linear model. Both m...
Extraction of Local Linear Models from TakagiSugeno Fuzzy Model with Application to Modelbased Predictive Control
 In Proceedings Seventh European Congress on Intelligent Techniques and Soft Computing EUFIT’99
, 1999
"... MBPC is a nice technique to control multivariable systems while dealing with constraints and certain objective. Linear MBPC (LMBPC) is currently a settled theory and can be applied straightforward for linear processes. In this paper we deal with nonlinear systems, for which linear models that can be ..."
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MBPC is a nice technique to control multivariable systems while dealing with constraints and certain objective. Linear MBPC (LMBPC) is currently a settled theory and can be applied straightforward for linear processes. In this paper we deal with nonlinear systems, for which linear models that can be extracted. This way a time varying linear representaion is obtained which is used in LMBPC. Different schemes to obtain such local linear models are assessed in the light of the achieved performance of the predictive controller. TakagiSugeno (TS) fuzzy models are chosen, because the model structure as local linear models can be derived from the linear rule consequences in a direct way.
INTERNATIONAL JOURNAL OF MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES Multiple
"... model modeling and predictive control of the pH neutralization process ..."
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