Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints

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

Volumn 37, Issue 2, 2007, Pages 425-436

  He, Pingan ^a   Jagannathan, S ^a  

^a Architectural and Environmental Engineering   (United States)

Author keywords

Approximate dynamic programming; Neural network control; Optimal control; Reinforcement learning

Indexed keywords

ACTUATORS; COMPUTER SIMULATION; CONSTRAINT THEORY; CONTROL EQUIPMENT; DISCRETE TIME CONTROL SYSTEMS; ERRORS; NONLINEAR CONTROL SYSTEMS; OPTIMAL CONTROL SYSTEMS; REINFORCEMENT LEARNING; STATE FEEDBACK;

ADAPTIVE-CRITIC-BASED NEURAL NETWORK CONTROLLER; APPROXIMATE DYNAMIC PROGRAMMING; CONTROLLER DESIGN; LEARNING NEURAL-NETWORK-BASED CONTROLLER;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; FEEDBACK SYSTEM; METHODOLOGY; NONLINEAR SYSTEM; REINFORCEMENT; SIGNAL PROCESSING; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FEEDBACK; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; REINFORCEMENT (PSYCHOLOGY); SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 34047138362     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2006.883869     Document Type: Article

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