Vibration Suppression Method for a Two-Link Flexible Manipulator Based on Adaptive Iterative Learning Algorithm

Liu, Chuanjie; Gao, Wenbin; Gao, Peng

doi:10.1007/s42417-023-00861-4

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…According to Hamilton's principle [15][16][17][18], the relationship between kinetic energy of system and potential energy, as well as non-conservative force, is expressed during the time interval from 1 t to 2 t : Where T is the kinetic energy of the flexible manipulator. U is the potential energy of the flexible manipulator, nc W is the power by non-conservative force of the flexible manipulator.…”

Section: Dynamic Modelmentioning

confidence: 99%

A New Fuzzy Backstepping Control Based on RBF Neural Network for Vibration Suppression of Flexible Manipulator

Wei,

Zheng,

Zhu

et al. 2024

Preprint

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Flexible manipulators have been widely used in industrial production. However, due to the poor rigidity of the flexible manipulator, it is easy to generate vibration. It will reduce the working accuracy and service life of the flexible manipulator. It is necessary to suppress the vibration during the operation of the flexible manipulator. Based on the energy method and Hamilton principle, the partial differential equations of the flexible manipulator are established. Secondly, an improved radial basis function (RBF) neural network is combined with the fuzzy backstepping method to identify and suppress the random vibration during the operation of the flexible manipulator, and the Lyapunov function and control law are designed. Finally, Simulink was used to build a simulation platform, three different external disturbances were set up, and the effect of vibration suppression was observed through the change curves of the final velocity error and displacement error. Compared with RBF neural network boundary control method and RBF neural network inversion method, the simulation results show that the effect of RBF neural network fuzzy inversion method is better than the previous two control methods, the system convergence is faster, and the equilibrium position error is smaller.

show abstract

Section: Dynamic Modelmentioning

confidence: 99%

A New Fuzzy Backstepping Control Based on RBF Neural Network for Vibration Suppression of Flexible Manipulator

Wei,

Zheng,

Zhu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Effective Suppression of Mover-Position- Dependent Resonances Using Combined Decoupled Persistent Oscillation Compensator and Disturbance Observer Structure

Kim,

Oh,

Yang

et al. 2024

IEEE Access

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A New Fuzzy Backstepping Control Based on RBF Neural Network for Vibration Suppression of Flexible Manipulator

Wei,

Zheng,

Zhu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Flexible manipulators have been widely used in industrial production. However, due to the poor rigidity of the flexible manipulator, it is easy to generate vibration. This will reduce the working accuracy and service life of the flexible manipulator. It is necessary to suppress vibration during the operation of the flexible manipulator. Based on the energy method and the Hamilton principle, the partial differential equations of the manipulator were established. Secondly, an improved radial basis function (RBF) neural network was combined with the fuzzy backstepping method to identify and suppress random vibration during the operation of the flexible manipulator, and the Lyapunov function and control law were designed. Finally, Simulink was used to build a simulation platform, three different external disturbances were set up, and the effect of vibration suppression was observed through the change curves of the final velocity error and displacement error. Compared with the RBF neural network boundary control method and the RBF neural network inversion method, the simulation results show that the effect of the RBF neural network fuzzy inversion method is better than the previous two control methods, the system convergence is faster, and the equilibrium position error is smaller.

show abstract

Vibration Suppression Method for a Two-Link Flexible Manipulator Based on Adaptive Iterative Learning Algorithm

Cited by 3 publications

References 37 publications

A New Fuzzy Backstepping Control Based on RBF Neural Network for Vibration Suppression of Flexible Manipulator

A New Fuzzy Backstepping Control Based on RBF Neural Network for Vibration Suppression of Flexible Manipulator

Effective Suppression of Mover-Position- Dependent Resonances Using Combined Decoupled Persistent Oscillation Compensator and Disturbance Observer Structure

A New Fuzzy Backstepping Control Based on RBF Neural Network for Vibration Suppression of Flexible Manipulator

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