Diesel engine identification and predictive control using Wiener and Hammerstein models

Air management process in a turbocharged diesel engine is a multivariable, highly coupled nonlinear system with fast dynamics. Because of this, control algorithms with reasonably low computation times (enabling real time application) must be used. Furthermore, testing of new algorithms on a real engine is expensive. Therefore, a detailed non-linear engine simulator based on a first principles model is developed. A brief description of this model is shown. Next, identification and control schemes based on model predictive control and Wiener and Hammerstein models are proposed. Finally, some results of these algorithms implemented on the engine simulator are offered, and compared with those obtained by applying standard generalized predictive control (GPC)