TY - GEN
T1 - Iterative learning based driver input synthesis for evaluating transient diesel soot emissions
AU - Ahlawat, Rahul
AU - Fathy, Hosam K.
AU - Stein, Jeffrey L.
PY - 2011/12/1
Y1 - 2011/12/1
N2 - This paper presents the synthesis of driver inputs for evaluating diesel soot emissions using iterative learning control. Transient soot emissions from diesel engine vehicles are extremely sensitive to driver aggressiveness. Using closed-loop tracking controllers to follow a vehicle over a prescribed drive cycle usually do not account for the fact that drivers potentially adapt their driving styles to a given powertrain design. This work develops an algorithm producing driver input traces that significantly reduces the soot emissions for a given drive cycle, thus providing a consistent basis for evaluating the influence of powertrain design changes on soot emissions. Possible improvements are first explored using conventional optimal techniques and results are obtained using linear programming. It is then shown that a first-order PD-type iterative learning control based algorithm can deliver good performance, substantially reducing the total soot emissions at a fraction of the computational cost.
AB - This paper presents the synthesis of driver inputs for evaluating diesel soot emissions using iterative learning control. Transient soot emissions from diesel engine vehicles are extremely sensitive to driver aggressiveness. Using closed-loop tracking controllers to follow a vehicle over a prescribed drive cycle usually do not account for the fact that drivers potentially adapt their driving styles to a given powertrain design. This work develops an algorithm producing driver input traces that significantly reduces the soot emissions for a given drive cycle, thus providing a consistent basis for evaluating the influence of powertrain design changes on soot emissions. Possible improvements are first explored using conventional optimal techniques and results are obtained using linear programming. It is then shown that a first-order PD-type iterative learning control based algorithm can deliver good performance, substantially reducing the total soot emissions at a fraction of the computational cost.
UR - http://www.scopus.com/inward/record.url?scp=84881454176&partnerID=8YFLogxK
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U2 - 10.1115/DSCC2011-6180
DO - 10.1115/DSCC2011-6180
M3 - Conference contribution
AN - SCOPUS:84881454176
SN - 9780791854761
T3 - ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011
SP - 643
EP - 650
BT - ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011
T2 - ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, DSCC 2011
Y2 - 31 October 2011 through 2 November 2011
ER -