Funnel nonsingular terminal sliding mode control based on TDE for robotic manipulators with model uncertainties

Wang, Huaizhen; Fang, Lijin; Song, Tangzhong; Hu, Menghui; Wu, Zhenghan

doi:10.1109/cac51589.2020.9327561

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…(2) The proposed method gets rid of the dependence on the system model. Unlike references, [2][3][4][5][6][7][8] the parameters in the network can often be effectively adjusted, which can well compensate the system dynamics, so as to effectively solve the problem of system modeling. (3) Unlike references, 3,5,7,9,11,12,20 which need to predict the upper bound of the system uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…However, the adaptive parameters in the fuzzy control are often adjusted too large, which makes other parameters difficult to be in the accurate range and can only be applied to a small number of systems. In references, 5,6 a sliding mode control scheme of manipulator based on time delay estimation (TDE) is proposed. Time delay estimation technology is used to compensate the system dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike references, [2][3][4][5][6][7][8] the parameters in the network can often be effectively adjusted, which can well compensate the system dynamics, so as to effectively solve the problem of system modeling. (3) Unlike references, 3,5,7,9,11,12,20 which need to predict the upper bound of the system uncertainty. A gain adaptive sliding mode control strategy which can estimate the system uncertainty in real time is proposed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Radial basis function neural network based second-order sliding mode control for robotic manipulator

Chen

Tang

Zhao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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An adaptive second-order sliding mode control method based on RBF neural network is proposed for n-DOF robotic manipulators in the presence of external disturbances. First, RBF neural network is used to approximate the model information. Second, by using adaptive technology to compensate the uncertainty, whose prior knowledge about upper bound is not required. In addition, since the proposed control scheme is continuous, the chattering phenomenon is almost completely eliminated. Finally, the stability and finite time convergence of the proposed method are proved by Lyapunov stability theory. Through the simulation of 2-DOF manipulator and 5-DOF manipulator, the effectiveness and superiority of the control scheme are verified.

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