Intelligent anti-swing control for bridge crane

A new intelligent anti-swing control scheme,which combined fuzzy neural network(FNN) and sliding mode control(SMC) with particle swarm optimization(PSO),was presented for bridge crane.The outputs of three fuzzy neural networks were used to approach the uncertainties of the positioning subsystem,lifting-rope subsystem and anti-swing subsystem.Then,the parameters of the controller were optimized with PSO to enable the system to have good dynamic performances.During the process of high-speed load hoisting and dropping,this method can not only realize the accurate position of the trolley and eliminate the sway of the load in spite of existing uncertainties,and the maximum swing angle is only ±0.1 rad,but also completely eliminate the chattering of conventional sliding mode control and improve the robustness of system.The simulation results show the correctness and validity of this method.

Load Reduction Test Method of Similarity Theory and BP Neural Networks of Large Cranes

Static load tests are an important means of supervising and detecting a crane＇s lift capacity. Due to space restrictions, however, there are difficulties and potential danger when testing large bridge cranes. To solve the loading problems of large-tonnage cranes during testing, an equivalency test is proposed based on the similarity theory and BP neural networks. The maximum stress and displacement of a large bridge crane is tested in small loads, combined with the training neural network of a similar structure crane through stress and displacement data which is collected by a physics simulation progressively loaded to a static load test load within the material scope of work. The maximum stress and displacement of a crane under a static load test load can be predicted through the relationship of stress, displacement, and load. By measuring the stress and displacement of small tonnage weights, the stress and displacement of large loads can be predicted, such as the maximum load capacity, which is 1.25 times the rated capacity. Experimental study shows that the load reduction test method can reflect the lift capacity of large bridge cranes. The load shedding predictive analysis for Sanxia 1200 t bridge crane test data indicates that when the load is 1.25 times the rated lifting capacity, the predicted displacement and actual displacement error is zero. The method solves the problem that lifting capacities are difficult to obtain and testing accidents are easily possible when 1.25 times related weight loads are tested for large tonnage cranes.

Natural Frequency and Main Vibration Mode Analysis of the Braking System of Metallurgical Crane

The safety brake system of metallurgy bridge crane is generally composed of two separated block brake, brake disc, and torsion shaft. The analysis of natural frequency and main vibration mode on this two-degree torsion vibration system is the basement to study the vibration model and vibration performance. In this work, we investigated natural frequency of the braking system of metallurgical crane with analytic method. This provides a systematic guidance towards a successful brake system design