Predictive control by local linearization of a Takagi-Sugeno fuzzy model

Linear model based predictive control (MBPC) has many advantages but also drawbacks over nonlinear MBPC. In this paper a possibility of using Linear MBPC to control nonlinear systems is investigated. Takagi-Sugeno fuzzy models are chosen as the model structure. Local linear models can be derived from the linear rule consequents in a straightforward way. Each sample time a local linear model is calculated and used to calculate the next incremental control action using Linear MBPC. This receding horizon controller is used in the IMC scheme to correct for model mismatch. Two simulation examples are given: a SISO liquid level process and a MIMO liquid level process with two inputs and four outputs.