Adaptive Neural Network Architectures For Emission Control of Engines (TSE-03G)

Missouri University Of Science And Technology, Rolla MO

Investigators

Abstract

The goal of this project is to provide the next generation emission controller for engines with adaptation, optimization, and learning so as to achieve low NOx and improved fuel economy. The controller will guarantee performance in the presence of unknown nonlinearities, feedback delays, and is supported by a rigorous design and mathematical framework. Specific objectives to support that goal are: 1. Develop a robust adaptive NN control scheme for SI engines that would minimize the effect cyclic dispersion at very lean operation (equivalence ratio =0.7) by selecting appropriate feedback parameters. 2. Study and model the complex dynamics in cyclic output for SI engines under the influence of high-levels of EGR. Investigate potential for reducing NOx over 50% below current EGR systems. 3. Develop a robust adaptive critic NN EGR control scheme that would minimize the effect of cyclic dispersion and would allow satisfactory performance in low NOx regimes via appropriate feedback. Provide methodology to integrate combustion stability with high levels of EGR in SI engines. 4. Simulate and verify the EGR and lean stability controller performance on an experimentally validated model. Demonstrate the controller schemes on a single cylinder engine in the laboratory. 5. Investigate the complex dynamics in output for diesel engines under the influence of high levels of EGR with an objective of reducing the NOx over 50% with minimal particulate matter. Provide recommendations about the applicability of the proposed EGR controllers for diesel engines. These projects will be pursued in collaboration with organizations such as Caterpillar, Inc. and Oak Ridge National Laboratory. These areas of research could lead to significant advances in the development of advanced control schemes for non-strict feedback nonlinear systems such as next generation spark ignition, diesel as well as nontraditional engines such as homogeneous charge compression ignition, direct injection spark ignition and hybrid engines (electric power and gasoline).