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International Journal of Control Volume 93, 2020 - Issue 10

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Finite-horizon integration for continuous-time identification: bias analysis and application to variable stiffness actuators

Dario PigaDalle Molle Institute for Artificial Intelligence, Manno, SwitzerlandCorrespondence[email protected]

Pages 2378-2391 | Received 15 Jun 2018, Accepted 30 Nov 2018, Published online: 18 Dec 2018

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https://doi.org/10.1080/00207179.2018.1557348
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Articles from other publishers (5)

Yuanxi Sun, Yuntao Tang, Jia Zheng, Dianbiao Dong & Long Bai. (2022) Optimal Variable Stiffness Control and Its Applications in Bionic Robotic Joints: A Review. Journal of Bionic Engineering 20:2, pages 417-435.
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Manas Mejari, Bojan Mavkov, Marco Forgione & Dario Piga. (2022) Direct identification of continuous-time LPV state-space models via an integral architecture. Automatica 142, pages 110407.
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Marco Forgione & Dario Piga. (2021) Continuous-time system identification with neural networks: Model structures and fitting criteria. European Journal of Control 59, pages 69-81.
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Manas Mejari, Bojan Mavkov, Marco Forgione & Dario Piga. (2021) An integral architecture for identification of continuous-time state-space LPV models. IFAC-PapersOnLine 54:8, pages 7-12.
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Bojan Mavkov, Marco Forgione & Dario Piga. (2020) Integrated Neural Networks for Nonlinear Continuous-Time System Identification. IEEE Control Systems Letters, pages 1-1.
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