Adaptive critic learning techniques for engine torque and air-fuel ratio control

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 38, Issue 4, 2008, Pages 988-993

  Liu, Derong ^a   Javaherian, Hossein ^b   Kovalenko, Olesia ^c   Huang, Ting ^a  

^a 1120 Science and Engineering Offices   (United States)

^b GENERAL MOTORS CORPORATION   (United States)

^c McMaster Carr   (United States)

Author keywords

Adaptive critic designs (ACDs); Adaptive dynamic programming; Air fuel ratio (AFR) control; Approximate dynamic programming; Automotive engine control; Torque control

Indexed keywords

AIR POLLUTION; AUTOMOBILE ENGINES; AUTOMOBILE PARTS AND EQUIPMENT; CELLULAR RADIO SYSTEMS; DYNAMIC PROGRAMMING; EDUCATION; ENGINES; HEURISTIC PROGRAMMING; IMAGE CLASSIFICATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL PROGRAMMING; PROJECT MANAGEMENT; REINFORCEMENT; REINFORCEMENT LEARNING; RESEARCH; SYSTEMS ENGINEERING;

ADAPTIVE CRITIC DESIGNS; ADAPTIVE CRITIC DESIGNS (ACDS); ADAPTIVE CRITIC LEARNING; ADAPTIVE DYNAMIC PROGRAMMING; AFR CONTROL; AIR-FUEL RATIO (AFR) CONTROL; AIR-FUEL RATIO CONTROL; APPROXIMATE DYNAMIC PROGRAMMING; AUTOMOTIVE ENGINE CONTROL; AUTOMOTIVE ENGINES; CONTROL PROBLEMS; ENGINE CALIBRATION; ENGINE TORQUE; EXHAUST AIR; HEURISTIC DYNAMIC PROGRAMMING; IMPROVED PERFORMANCE; LEARNING CONTROL; MODEL-FREE; NEURAL NETWORK CONTROLLERS; NEURAL NETWORK MODELLING; NEW APPROACHES; OPERATING CONDITIONS; OPTIMAL CONTROL; OPTIMUM PERFORMANCE; RESEARCH PROJECTS; RESEARCH RESULTS; SELF-LEARNING CONTROL; SELF-LEARNING NEURAL NETWORKS; TEST VEHICLES; TORQUE CONTROL; TRANSIENT PERFORMANCE;

NEURAL NETWORKS;

FUEL OIL;

AIR; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FEEDBACK SYSTEM; POWER SUPPLY; SYSTEM ANALYSIS; THEORETICAL MODEL; TORQUE;

AIR; ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; ELECTRIC POWER SUPPLIES; FEEDBACK; FUEL OILS; MODELS, THEORETICAL; PROGRAMMING, LINEAR; TORQUE;

EID: 49049108697     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2008.922019     Document Type: Article

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