Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 43, Issue 2, Pages 779-789Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2012.2216523
Keywords
Adaptive dynamic programming (ADP); approximate dynamic programming; finite approximation errors; neural networks; optimal control
Categories
Funding
- National Natural Science Foundation of China [60904037, 60921061, 61034002]
- Beijing Natural Science Foundation [4102061]
- China Postdoctoral Science Foundation [201104162]
Ask authors/readers for more resources
In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal control problems for infinite-horizon discrete-time nonlinear systems with finite approximation errors. The idea is to use an iterative ADP algorithm to obtain the iterative control law that makes the iterative performance index function reach the optimum. When the iterative control law and the iterative performance index function in each iteration cannot be accurately obtained, the convergence conditions of the iterative ADP algorithm are obtained. When convergence conditions are satisfied, it is shown that the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some mild assumptions. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available