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• W2890392025 endingPage "2063" @default.
• W2890392025 startingPage "2054" @default.
• W2890392025 abstract "Recently, Recurrent Neural Network (RNN) control schemes for redundant manipulators have been extensively studied. These control schemes demonstrate superior computational efficiency, control precision, and control robustness. However, they lack planning completeness. This paper explains why RNN control schemes suffer from the problem. Based on the analysis, this work presents a new random RNN control scheme, which 1) introduces randomness into RNN to address the planning completeness problem, 2) improves control precision with a new optimization target, 3) improves planning efficiency through learning from exploration. Theoretical analyses are used to prove the global stability, the planning completeness, and the computational complexity of the proposed method. Software simulation is provided to demonstrate the improved robustness against noise, the planning completeness and the improved planning efficiency of the proposed method over benchmark RNN control schemes. Real-world experiments are presented to demonstrate the application of the proposed method." @default.
• W2890392025 created "2018-09-27" @default.
• W2890392025 creator A5025128323 @default.
• W2890392025 creator A5065912071 @default.
• W2890392025 creator A5074967911 @default.
• W2890392025 date "2019-04-01" @default.
• W2890392025 modified "2023-09-24" @default.
• W2890392025 title "A Model-Based Recurrent Neural Network With Randomness for Efficient Control With Applications" @default.
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• W2890392025 doi "https://doi.org/10.1109/tii.2018.2869588" @default.
• W2890392025 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6934362" @default.
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• W2890392025 hasPublicationYear "2019" @default.
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