A boundedness result for the direct heuristic dynamic programming

DOI10.1016/j.neunet.2012.02.005zbMath1254.90286DBLPjournals/nn/LiuSSGM12OpenAlexW2005675298WikidataQ51407734 ScholiaQ51407734MaRDI QIDQ1929722

Yanyan Li

Publication date: 9 January 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2012.02.005

zbMATH Keywords

Lyapunov stabilityapproximate dynamic programming (ADP)direct heuristic dynamic programming (direct HDP)uniformly ultimately boundedness (UUB)

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Dynamic programming (90C39)

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Cites Work

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• \({\mathcal Q}\)-learning
• Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach
• Policy Iterations on the Hamilton–Jacobi–Isaacs Equation for $H_{\infty}$ State Feedback Control With Input Saturation

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