论文信息：Huili Chen, Guoliang Liu, Guohui Tian, Jianhua Zhang, Ze Ji, Safe Distance Prediction for Braking Control of Bridge Cranes Considering Anti-Swing, International Journal of Intelligent System, Accepted.

摘要：

The crane is an important machine for lifting and moving heavy objects in human coexistence dynamic environments. The suddenly appeared workers, vehicles and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues of the bridge cranes based on its physical state and control model. Due to the swing of the payload, safe braking distance is not a constant value. Therefore, we here propose a model prediction control (MPC) based anti-swing method for a non-zero initial velocity state, and then an offline learning mechanism is introduced to learn a statistical model between the velocity of the crane and the safe braking distance. In this way, we can predict how far the crane requires to safely stop without swing based on its current velocity, which is the safe distance prediction to evaluate the dangerous level of the dynamic obstacle. The experiments using the simulated and real cranes demonstrate that the proposed safe distance prediction method is effective for safe braking control of the bridge cranes

基金资助：国家重点研发计划“智能机器人”重点专项课题，高速高精度智能天车物料调度安全作业系统，课题号：2018YFB1306504，执行年限：2019年06月-2022年05月