Robust Time-Optimal Kinematic Control of Robotic Manipulators Based on Recurrent Neural Network Against Harmonic Noises
Kuang, Yiqun; Li, Shuai; Li, Zhan (2025-04-25)
25.04.2025
Kuang, Y., Li, S., & Li, Z. (2025). Robust Time-Optimal Kinematic Control of Robotic Manipulators Based on Recurrent Neural Network Against Harmonic Noises. Actuators, 14(5), 213. https://doi.org/10.3390/act14050213
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202505133329
https://urn.fi/URN:NBN:fi:oulu-202505133329
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Abstract
Industrial and service manipulators demand implementing time-optimal kinematic control to minimize task duration in a manner of maximizing end-effector velocity during path tracking. However, achieving this objective in the presence of harmonic noise while strictly enforcing joint motion constraints remains a significant challenge. This paper introduces a novel approach that leverages dynamic recurrent neural networks (RNNs) within a constrained optimization framework to deliver robust, time-optimal kinematic control even under harmonic disturbances. We provide a thorough theoretical analysis of the RNN-based control solver, establishing its convergence and optimality. Importantly, our method maximizes end-effector speed without violating any joint velocity limits, thereby enhancing the path-tracking speed compared to previous schemes. Simulation results and physical experiments further demonstrate the effectiveness and superiority of the proposed approach.
Industrial and service manipulators demand implementing time-optimal kinematic control to minimize task duration in a manner of maximizing end-effector velocity during path tracking. However, achieving this objective in the presence of harmonic noise while strictly enforcing joint motion constraints remains a significant challenge. This paper introduces a novel approach that leverages dynamic recurrent neural networks (RNNs) within a constrained optimization framework to deliver robust, time-optimal kinematic control even under harmonic disturbances. We provide a thorough theoretical analysis of the RNN-based control solver, establishing its convergence and optimality. Importantly, our method maximizes end-effector speed without violating any joint velocity limits, thereby enhancing the path-tracking speed compared to previous schemes. Simulation results and physical experiments further demonstrate the effectiveness and superiority of the proposed approach.
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