Neural Dynamic Programming for Automotive Engine Control

University Of Illinois At Chicago, Chicago IL

Investigators

Abstract

Neural dynamic programming (NDP) is a scheme that provides approximate solutions to dynamic programming in which neural networks are used as a tool for function approximation. Such a scheme is applicable to problems that minimize/maximize a cost but for which a traditional dynamic programming approach is not feasible since the cost function is usually not known. In the present project, applications of NDP to automotive engine control will be studied and implemented. A challenging problem facing the automotive industry is to design vehicles that generate emissions satisfying the federal government's future emission regulations. The challenge here is to minimize the emission and at the same time to achieve better fuel economy and vehicle driveability. In the past few years, the automotive industry has engaged in efforts to develop engine control algorithms that will generate emissions satisfying the government's emission standards. Emissions generated by automobiles are one of the major sources for air pollution in the United States. Theoretically, emissions can be controlled to a minimum possible level by controlling the engine combustion process so that the air and fuel are mixed at certain desired ratio. This control problem, as it is known, turns out to be very difficult to solve. This is partly due to the complexity of modern automotive engines and due to the complexity of the fuel combustion process. In addition to achieving lower emissions, the automotive industry has also engaged in efforts to design cars that have better driveability and consume less fuel NDP implementation can be accomplished by adding a $1-10 chip developed under a previous NSF SBIR grant that, with additional training, could also be to reduce the cost of fuel flexibility an urgent strategic need.