Exact and Linearized Neural Predictive Control: A Turbocharged SI Engine Example

Abstract

This paper describes a real-time control method for non-linear systems based on model predictive control. The model used for the prediction is a neural network because of its ability to represent non-linear systems, its ability to be differentiated, and its simplicity of use. The feasibility and the performance of the method, based on on-line linearization, are demonstrated on a turbocharged spark-ignited engine application, where the simulation models used are very accurate and complex. The results, first in simulation and then on a test bench, show the implementation of the proposed control scheme in real time.