Title: Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis
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| Authors: Wei, QL; Liu, DR; Lin, Q
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| Author Full Names: Wei, Qinglai; Liu, Derong; Lin, Qiao
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| Source: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 28 (11):2490-2502; 10.1109/TNNLS.2016.2593743 NOV 2017
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| Language: English
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| Abstract: 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
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| eISSN: 2162-2388
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| IDS Number: FK3RN
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| Unique ID: WOS:000413403900003
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| PubMed ID: 27529879
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