一种基于神经网络算法的桥式起重机防摇摆控制方法
By using neural network algorithms to predict the swaying trend of the load on a bridge crane and performing feedforward and nonlinear compensation, the problems of prediction lag and insufficient adaptability in existing technologies are solved, achieving high-precision and robust anti-sway control.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUANENG LANCANG RIVER HYDROPOWER CO LTD
- Filing Date
- 2025-05-19
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies struggle to accurately predict the future swaying trend of overhead crane loads, resulting in delayed control signals that cannot effectively suppress large swaying. Furthermore, nonlinear compensation cannot adapt to changes in the crane's operating environment, reducing control accuracy and robustness.
A sway control method based on neural network algorithm is adopted. The sway trend of the hoist is predicted by dynamic model. Combined with feedforward compensation and nonlinear compensation, variational autoencoder and cyclic dynamic mapping are used for accurate prediction. Combined with linear quadratic regulator for real-time correction, dynamic adjustment of trolley motion is realized.
It improves the accuracy of predicting future swing states, enhances the response speed and adaptability of the control system, improves control precision and robustness, reduces load swing, and ensures system stability.
Smart Images
Figure CN120504253B_ABST