Research On Intelligent Crane Anti-sway Control System Based On Iterative Learning

The crown block system is widely used in industrial production sites and storage,transportation links,and occupies a pivotal position in industrial applications.Nowadays,with the rapid development of cranes in the direction of large-scale,intelligent,rapid automation,and accuracy of products,the performance of transportation and reliability of transportation are also increasingly improved,and the control of intelligent crane systems is difficult.Lies in how to reduce the amplitude of the swing angle of the crane load.The load of the crane system and the trolley are connected by steel cables.The crane system can only indirectly control the swing angle of the load through the driving track of the console car.The operating environment of the vehicle system is quite complicated,so it is quite difficult to control the amplitude of the load swing angle as much as possible while ensuring the transportation efficiency.In this paper,the trajectory tracking method is used to control the crane to follow the desired target trajectory with a smaller preset swing angle amplitude,so that the swing angle amplitude of the crane load meets the control performance index during the lifting process.In order to solve the problem of anti-sway control of the intelligent crown crane system,this article studies from the aspects of dynamic modeling of the crown crane system,proposes a new iterative learning algorithm,verifies the control effect,and builds a simulation system for the intelligent crown crane anti-sway control.First of all,according to the definition of physics and energy method,the detailed analysis of the working process of the crown crane is carried out.After fully considering and modeling the interference links such as friction and air resistance,the dynamic modeling of the crown crane system is carried out.Establish the mathematical model of the input and output of the idealized angle of the crown block,and analyze and explain the iterative learning control algorithm model.Then this paper establishes a simulation model of the crown swing load swing amplitude of the crane,compares and analyzes the various factors affecting the crane swing swing amplitude,and analyzes the control goals and control difficulties of the crane during tracking control.Then this paper analyzes the model of the crown block system and selects the iterative learning algorithm to adapt to the characteristics of the nonlinear,under-driven and strong coupling of the crown block system.According to the convergence speed of the crown block system and the control requirements of anti-random interference,a new adaptive nonlinear variable gain index PD open-loop iterative learning algorithm is proposed,and the convergence of the applied algorithm is strictly verified.Then,based on the control input and angle output data during the actual travel of the crown block,the article corrects the crown block friction,air resistance,and ideal angle output models proposed in Chapter 2 of this article and integrates them into the crown block system model.In this paper,the experimental platform of the intelligent crown crane antisway control system is built in the environment of MATLAB / Simulink,and the algorithm proposed in Chapter 3 of this paper is experimentally verified.The reliability and superiority of the mathematical model of the crown block system derived from this paper and the adaptive nonlinear variable gain index PD open-loop iterative learning algorithm are fully verified.Finally,this paper designs and builds an intelligent crane anti-sway control simulation system based on all the above results.The system includes a hardware structure and a control system;the hardware structure includes a bridge structure,a trolley structure,and a transmission structure.This paper uses the SIMULATION function of SOLIDWORKS to perform all Stress stability and deformation analysis;the control system includes the core controller and the motion control system;this article realizes the anti-sway control of the intelligent crown crane simulation test system by organically integrating the hardware structure and the control system.