Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management

IEEE Transactions on Control Systems Technology

Volumn 22, Issue 3, 2014, Pages 1018-1031

  Cairano, Stefano Di ^a,b   Bernardini, Daniele ^c   Bemporad, Alberto ^c   Kolmanovsky, Ilya V ^d  

^a FORD MOTOR COMPANY   (United States)

^b MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

^c IMT SCHOOL FOR ADVANCED STUDIES LUCCA   (Italy)

^d The Department of Civil and Environmental Engineering   (United States)

Author keywords

Automotive controls; driver machine interaction; energy management; model predictive control (MPC); optimization; real time learning; stochastic control.

Indexed keywords

CONTROL SYSTEM SYNTHESIS; ENERGY MANAGEMENT; MARKOV PROCESSES; MODEL PREDICTIVE CONTROL; OPTIMIZATION; QUADRATIC PROGRAMMING; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC SYSTEMS;

AUTOMOTIVE CONTROL; BATTERY STATE OF CHARGE; DRIVER-MACHINE INTERACTION; REAL-TIME LEARNING; REAL-WORLD DRIVING CYCLES; STOCHASTIC CONTROL; STOCHASTIC DYNAMIC PROGRAMMING; STOCHASTIC OPTIMIZATIONS;

PREDICTIVE CONTROL SYSTEMS;

EID: 84899960267     PISSN: 10636536     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCST.2013.2272179     Document Type: Article

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