Smart Engines: Fuel Flexible Engine Control using Adaptive Neural Network Critics

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AbstractLow temperature combustion engines such as homogeneous charge compression ignition (HCCI) offer fuel flexibility with high fuel efficiency and low emissions. If the type and composition of the fuel, such as bio-fuel, is not known a-priori, a ?smart? engine has to be capable of sensing heat release and adjusting combustion system parameters online for minimized emissions and fuel consumption. This necessitates a more advanced adaptive control schemes for the control of these types of complex non-affine nonlinear systemsThe overall goal of this study is to provide the next generation adaptive critic neural net controllers for complex non-affine, nonlinear systems supported by a rigorous and repeatable design and mathematical framework. The controller performance will be validated for the HCCI engine for a range of bio-mass based fuel stocks using conventional and novel input sensors for measuring cyclic heat release. Intellectual Merit: The project will advance the state of the art in Adaptive Dynamic Programming for control by providing rigorous mathematical analysis for convergence and stability, and performance guarantees in the presence of approximation errors, actuator constraints and delays. Moreover, by applying the theoretical results to an emerging control application of fuel-flexible engines, this type of controllers will be implemented and tested in hardware in the Co-PI's internal combustion engine laboratory.Broader Impact: Improved control of next generation fuel-flexible engines and multi mode engines, such as plug-in hybrids, is expected to improve fuel efficiency and reduce harmful emission, thus directly impacting the environment and reducing dependence on foreign oil. Research results will be integrated as part of undergraduate course and laboratories. Dissemination plans include distribution of software through websites, patents, journal and conference publications. The PIs have a track record of hiring underrepresented minorities through MST?s Minority Engineering Program, extending research opportunities to undergraduates via REU supplements and interactions with EPSCOR states. International collaborations will be pursued. Technology transfer to industrial members is planned through the NSF I/UCRC Site on Intelligent Maintenance Systems where the PI is the Site Director.