Approximating Optimal feedback Controllers of Finite Horizon Control Problems Using Hierarchical Tensor Formats
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Publication:5084512
DOI10.1137/21M1412190zbMath1491.49021arXiv2104.06108OpenAlexW3156706052WikidataQ114074054 ScholiaQ114074054MaRDI QIDQ5084512
Leon Sallandt, Mathias Oster, Reinhold Schneider
Publication date: 24 June 2022
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.06108
Bellman equationfeedback controlmodel predictive controlpolicy iterationPontryagin maximum principletensor train
Optimal feedback synthesis (49N35) Multilinear algebra, tensor calculus (15A69) Optimality conditions for problems involving ordinary differential equations (49K15) PDE constrained optimization (numerical aspects) (49M41)
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Low-rank tensor methods for partial differential equations ⋮ Data-Driven Tensor Train Gradient Cross Approximation for Hamilton–Jacobi–Bellman Equations ⋮ Optimal polynomial feedback laws for finite horizon control problems ⋮ Approximation of optimal control problems for the Navier-Stokes equation via multilinear HJB-POD
Uses Software
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