Stable iterative adaptive dynamic programming algorithm with approximation errors for discrete-time nonlinear systems

In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained,...

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Veröffentlicht in:Neural computing & applications Jg. 24; H. 6; S. 1355 - 1367
Hauptverfasser: Wei, Qinglai, Liu, Derong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.05.2014
Springer
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Control system synthesis
Control theory. Systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Learning and adaptive systems
Optimal control
Original Article
Probability and Statistics in Computer Science
Theoretical computing
Approximate dynamic programming
Adaptive dynamic programming
Neural networks
Nonlinear systems
Optimal control
Adaptive critic designs
Policy
On line
Stability
Reinforcement learning
Control program
Non linear control
Error bound
Iterative method
Neural network
Non linear system
Indirect method
Discrete time
Approximation error
Off line
Infinite horizon
Dynamic programming
Optimal control (mathematics)
ISSN:0941-0643, 1433-3058
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Beschreibung
Zusammenfassung:In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. When the iterative control law and iterative performance index function in each iteration cannot be accurately obtained, it is shown that the iterative controls can make the performance index function converge to within a finite error bound of the optimal performance index function. Stability properties are presented to show that the system can be stabilized under the iterative control law which makes the present iterative ADP algorithm feasible for implementation both on-line and off-line. Neural networks are used to approximate the iterative performance index function and compute the iterative control policy, respectively, to implement the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-013-1361-7