A framework of iterative learning control under random data dropouts: Mean square and almost sure convergence

DOI10.1002/acs.2802zbMath1383.93100OpenAlexW2745107322MaRDI QIDQ4634728

Dong Shen, Jian-Xin Xu

Publication date: 11 April 2018

Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/acs.2802

zbMATH Keywords

Markov chainalmost sure convergenceiterative learning controlBernoulli modelmean square convergencedata dropoutstochastic sequence

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Learning and adaptive systems in artificial intelligence (68T05) Discrete-time control/observation systems (93C55) Linear systems in control theory (93C05) Stochastic learning and adaptive control (93E35) Stochastic systems in control theory (general) (93E03)

Related Items (4)

Robust finite‐iteration tracking of discrete‐time systems in repetitive process setting via ILC scheme ⋮ Finite‐level uniformly quantized learning control with random data dropouts ⋮ A comparison of compensation methods for random input data dropouts in networked iterative learning control system ⋮ Iterative learning control using faded measurements without system information: a gradient estimation approach

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