Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 28; H. 11; S. 2490 - 2502
Hauptverfasser: Wei, Qinglai, Liu, Derong, Lin, Qiao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adaptive algorithms
Adaptive critic designs
adaptive dynamic programming (ADP)
Aerospace electronics
Algorithms
approximate dynamic programming
Approximation algorithms
Computer simulation
Criteria
Discrete time systems
Dynamic programming
Heuristic algorithms
Iterative algorithms
local iteration
neural networks
neurodynamic programming
Nonlinear control
Nonlinear systems
Optimal control
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2016.2593743