Convolution model based predictive controller for a nonlinear process

The model predictive control (MPC) of a distributed parameter nonlinear lab oratory heating system is studied. A nonlinear convolution model consisting of a linear dynamic and a nonlinear steady-state part is applied as the mo del of the process in the MPC algorithm. The dynamic part is represented by a relative impulse response model (IRM). The steady-state gain is derived from the first principle model of the system. The application of this speci al convolution model is as simple as the use of the transfer function model ; however, it is valid on the whole operating range. MPC algorithms employi ng different models of the process are compared by simulation and physical tests.