Recursive estimation for reduced-order state-space models using polynomial chaos theory applied to vehicle mass estimation

IEEE Transactions on Control Systems Technology

Volumn 22, Issue 1, 2014, Pages 224-229

  Pence, Benjamin L ^a   Fathy, Hosam K ^b   Stein, Jeffrey L ^c  

^a FORD MOTOR COMPANY   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Detection; estimation; polynomial chaos theory; reduced order models; vehicle mass estimation

Indexed keywords

MODEL APPROXIMATIONS; PARAMETER ESTIMATORS; POLYNOMIAL CHAOS THEORY; RECURSIVE ESTIMATION; REDUCED ORDER MODELS; SIGNALTONOISE RATIO (SNR); STATE SPACE SYSTEMS; VEHICLE MASS;

CHAOS THEORY; ERROR DETECTION; MAXIMUM LIKELIHOOD ESTIMATION; SIGNAL TO NOISE RATIO; VEHICLES;

ESTIMATION;

EID: 84891557342     PISSN: 10636536     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCST.2013.2252349     Document Type: Article

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