Error Compensation for Long Arm Manipulator Based on Deflection Modeling and Neural Network
Haoying Li, Chenhao Fang, Jinze Shi, Baocheng Zeng, Chunlin Zhou · 2021 IEEE 4th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) · 2021
Long arm manipulators are designed to work in special conditions including aviation, engineering, and other scenarios that require a large span operation. However, since the long arm will cause a large flexible error, the manipulators maintain large terminal absolute error and difficulty in control. In addition, testing in a real machine is time and economic consuming, and obtaining enough data is untoward. Under such conditions, this paper proposes an error compensation method for a long arm manipulator combining deflection error modeling and neural network, using a specially designed long arm manipulator. By using this method, better results are achieved than traditional error modeling alone since non-traceable errors are also compensated for. The result is also better than neural network compensation alone since in the case of less training data preferable results can still be achieved.