Nonlinear process identification for control is studied. In identification test, the process is only tested (excited) along its operating-trajectory that includes various working points and transition periods. In model identification, a linear parameter varying (LPV) model is used. First linear models are identified using data sets at various working-points exclusive transition data; then the LPV model is identified by interpolating the linear models using total data. Sufficient conditions for a unique solution in parameter estimation will be given. Simulation study will be used to verify the effectiveness of the method. The identified model is suitable for model predictive control (MPC). 1.
|Title of host publication||Proceedings of the 17th IFAC World Congress, July 6 -11 2008, Seoul, South Korea|
|Editors||H. Shim, P.M.M.J. Chung, Hyung Suck Cho|
|Place of Publication||Oxford|
|Publication status||Published - 2008|