Near optimal output feedback control of nonlinear discrete-time systems based on reinforcement neural network learning

IEEE/CAA Journal of Automatica Sinica

Volumn 1, Issue 4, 2014, Pages 372-384

  Zhao, Qiming ^a   Xu, Hao ^b   Jagannathan, Sarangapani ^c  

^a Denso International America Inc   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c University of Missouri Rolla   (United States)

Author keywords

approximate dynamic programming; Finite horizon; Hamilton Jacobi Bellman equation; neural network; optimal regulation

Indexed keywords

CLOSED LOOP SYSTEMS; DIGITAL CONTROL SYSTEMS; DYNAMIC PROGRAMMING; ERRORS; FEEDBACK; LEARNING SYSTEMS; NEURAL NETWORKS; NONLINEAR EQUATIONS; REINFORCEMENT LEARNING;

APPROXIMATE DYNAMIC PROGRAMMING; DISCRETE - TIME SYSTEMS; FINITE HORIZONS; HAMILTON JACOBI BELLMAN EQUATION; HAMILTON-JACOBI-BELLMAN EQUATIONS; NONLINEAR DISCRETE-TIME SYSTEMS; OPTIMAL REGULATION; TIME-DEPENDENT ACTIVATION;

DISCRETE TIME CONTROL SYSTEMS;

EID: 84921361841     PISSN: 23299266     EISSN: 23299274     Source Type: Journal    
DOI: 10.1109/JAS.2014.7004665     Document Type: Article

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