Adaptive Neural Computed Torque Control for Robot Joints With Asymmetric Friction Model

The nonlinearity and uncertainty of dynamics pose significant challenges to ensuring the tracking performance of joint trajectories, especially time-varying effects on the load and temperature. In this letter, we present an adaptive neural computed torque control scheme to improve the tracking accur...

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Veröffentlicht in:	IEEE robotics and automation letters 2025-01, Vol.10 (1), p.732-739
Hauptverfasser:	Luo, Ruiqing, Hu, Zhengtao, Liu, Menghui, Du, Liang, Bao, Sheng, Yuan, Jianjun
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Adaptive control asymmetric friction modeling Asymmetry Asymptotic methods Boundary layers Compensators Computation Computational modeling computed torque control Friction Load modeling Mathematical models Neural networks Nonlinearity Parameter uncertainty Payloads Radial basis function radial basis function neural network Robot control Robots Sliding mode control Temperature dependence Torque Torque control Tracking trajectory tracking Uncertainty Vectors
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