Model reduction for process control using iterative nonlinear identification

Given a complex first principles model of a process, a strategy for model complexity reduction is developed, such that the model obtained is suitable for process control. The system is assumed to have a Volterra representation that can be parametrized in terms of basis functions with fixed poles. The approach taken consists of an iteratively using system identification techniques on the complex system model, while at the same time optimizing the inputs used. The results are tested on a copolymerization reactor example.