Fuzzy model-based predictive control using Takagi-Sugeno models

Nonlinear model-based predictive control (MBPC) in multi-input multi-output (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 Takagi±Sugeno fuzzy models in combination with MBPC is described. First, the fuzzy model-identi®cation of MIMO processes is given. The process model is derived from input±output data by means of product-space fuzzy clustering. The MIMO model is represented as a set of coupled multi-input, single-output (MISO) models. Next, the Takagi±Sugeno fuzzy model is used in combination with MBPC. The critical element in nonlinear MBPC is the optimization routine which is nonconvex and thus dicult to solve. Two methods to deal with this problem are developed: (i) a branch-and-bound method with iterative grid-size reduction, and (ii) control based on a local linear model. Both methods have been tested and evaluated with a simulated laboratory setup for a MIMO liquid level process with two inputs and four outputs. Ó 1999 Elsevier Science Inc. All rights reserved.