期刊
NEURAL COMPUTING & APPLICATIONS
卷 22, 期 2, 页码 219-227出版社
SPRINGER
DOI: 10.1007/s00521-011-0707-2
关键词
Adaptive critic designs; Adaptive dynamic programming; Approximate dynamic programming; Neural dynamic programming; Neural networks; Optimal control; Reinforcement learning
资金
- National Natural Science Foundation of China [60874043, 60904037, 60921061, 61034002]
- Beijing Natural Science Foundation [4102061]
- National Science Foundation of USA [ECCS-1027602]
In this paper, a novel neural-network-based iterative adaptive dynamic programming (ADP) algorithm is proposed. It aims at solving the optimal control problem of a class of nonlinear discrete-time systems with control constraints. By introducing a generalized nonquadratic functional, the iterative ADP algorithm through globalized dual heuristic programming technique is developed to design optimal controller with convergence analysis. Three neural networks are constructed as parametric structures to facilitate the implementation of the iterative algorithm. They are used for approximating at each iteration the cost function, the optimal control law, and the controlled nonlinear discrete-time system, respectively. A simulation example is also provided to verify the effectiveness of the control scheme in solving the constrained optimal control problem.
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