Abstract
In this paper, an adaptive control method based on radial basis function(RBF) neural network is proposed for the force control of 3R manipulator in the workspace. The control goal is to make the manipulator track the impedance trajectory to realize the flexible contact between the manipulator and the environment. Firstly, the Lagrangian-Euler method is used to model the dynamic of 3R manipulator. According to the characteristics of the dynamical model of 3R manipulator, a RBF neural network is adopted to design the adaptive controller, and the stability of the controller is analyzed by using Lyapunov criterion. Through the Simulink module in MATLAB, the effectiveness of the proposed algorithm is verified.
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Acknowledgements
This work was supported by Tianjin Natural Science Foundation of China (20JCYBJC01060), the National Natural Science Foundation of China (62103203, 61973175), and the Fundamental Research Funds for the Central Universities, Nankai University(63211120).
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Wang, D., Li, M., Wang, F., Liu, Z., Chen, Z. (2022). Adaptive Control of 3R Manipulator in the Workspace Based on Radial Basis Function Neural Network. In: Jia, Y., Zhang, W., Fu, Y., Zhao, S. (eds) Proceedings of 2022 Chinese Intelligent Systems Conference. CISC 2022. Lecture Notes in Electrical Engineering, vol 950. Springer, Singapore. https://doi.org/10.1007/978-981-19-6203-5_43
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