Research On Electronic Anti-swing Control Strategy Of Intelligent Crane

As an important tool for infrastructure construction and material transportation,lifting machinery needs to complete intelligent application upgrades in the structural transformation of national industrialization.The high standard requirements for production efficiency in intelligent manufacturing workshops require the safe and efficient continuous operation of overhead traveling cranes,a common logistics and transportation facility in the workshop,in complex working environments.The key technologies are accurate positioning of the transportation process and effective anti swing technology.This paper studies the electronic anti roll control strategy of cranes in the intelligent workshop scenario,and designs and implements a positioning anti roll control strategy that meets the requirements of the intelligent workshop.The main content of this article is as follows:1.For the bridge crane system,a dynamic model of the crane system is established using Lagrange mechanical equations,and the dynamic characteristics of a class of nonlinear systems such as cranes are analyzed based on the model equations,providing a theoretical basis for controller design.2.In order to meet the performance requirements of intelligent workshops for both precise positioning and load anti-swing of cranes,fractional application is used to improve the passive control strategy.The stability proof of passive analysis is simple,and fractional coupling improves model accuracy.The simulation results verify the improvement of positioning and anti-swing control performance in the process of approaching the target point,reducing load swing compared to conventional passive controllers,and the controller has good robustness and anti-interference.3.In order to improve the adaptability of the crane positioning anti roll controller to the multi working environment in the intelligent workshop,based on the improved multi-objective particle swarm optimization algorithm,the automatic tuning of the controller parameters is realized.Simulation results demonstrate that the parameter curve of the control strategy is adaptive to multiple operating conditions,and performs well in controlling multiple objective functions such as crane adjustment time and position error.4.To verify the control strategy mentioned earlier,in order to improve the production efficiency of intelligent workshops and meet the requirements of scheduling tasks,a crane control algorithm combined with offline trajectory planning is considered.The commonly used three-segment trajectory of bridge cranes is smoothed using Bessel curves,and a tracking controller is designed.The experimental results of establishing a verification platform show that the smoothed segmented trajectory performs well in positioning and anti roll targets,with high trajectory tracking efficiency,and that the smoothed trajectory can effectively reduce the impact on lifting machinery.