Identification of multiple linear models for nonlinear processes

This work presents a nonlinear dynamic model based on several local linear models under different operating conditions. Response of the global nonline ar dynamic model is also derived by weighting the sum of all local linear m odel outputs. In addition, the fuzzy set theory is applied to account for t he weighting factors for the local models. Also presented herein are two no vel means of estimating the multiple linear models' output: the parameter i nterpolation method and the output difference interpolation method. Accordi ng to our results, these two methods are identical in terms of interpolatin g the difference of state vector, outputs, and inputs. Some major identific ation methods, e.g., linearization of the first-principle model, identifica tion of linear local models, and least squares algorithm, are proposed. Sev eral typical nonlinear processes are used to demonstrate the effectiveness of the multiple linear model identification.