Neural Network Control of Spark Ignition Engines with High EGR Levels

— Research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% to 25% exhaust gas recirculation (EGR) in spark ignition (SI) engines [1]. However under high EGR levels the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance. A suite of neural network (NN)-based output feedback controllers with and without reinforcement learning is developed to control the SI engine at high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The stability analysis of the closed loop system is giv...

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Atmika Singh, Jonathan Blake Vance, Brian C. Kaul, Sarangapani Jagannathan, James A. Drallmeier
• Keywords: EGR Levels, Engine, IJCNN 2006, NN Observer