Research On PD-type Iterative Learning Control In Magnetic Levitation Ball Control System

The magnetic levitation system mainly uses the electromagnetic force to balance the object's own gravity,so that the object can be suspended,supported and vibration isolated.With advantages of no contact,zero friction,energy saving,etc.,the magnetic levitation system is widely applied in the forefront of rail transportation,precision manufacturing,biomedicine and aerospace.The magnetic levitation ball system has a single-degree-of-freedom magnetic levitation structure and has high requirements on the real-time performance of the controller.It is a typical experimental platform for verifying the magnetic levitation control algorithm,and the research results can be transplanted into more complex multidegree-of-freedom magnetic levitation systems.Its control technology is one of the core technologies of magnetic levitation systems.The technical difficulties lie in that magnetic levitation systems are highly nonlinear and open-loop unstable,while it is difficult to describe them with accurate mathematical models.Aiming at the control difficulties of the magnetic levitation ball system,this paper proposes an iterative learning control(ILC)algorithm for the moving control of the levitation ball.The core of the algorithm is to construct a convergent iterative formula,or iterative learning law,using historical or current error signals to modify the control signal,and finally to obtain the ideal control signal function to achieve the desired control effect.The control effect of the iterative learning algorithm is not highly dependent on the accuracy of the system's mathematical model,and it is not sensitive to changes in system parameters,so it has high adaptability and robustness.It can be applied not only to the motion control of the maglev system,but also to the control of the maglev vibration isolation platform.This paper takes the control of the magnetic levitation ball system as the main research topic,introduces the structure and operating principle of the magnetic levitation ball system,and establishes a mathematical model of the magnetic levitation ball system based on mathematical equations as the basis for estimating the controller parameters and simulation experiments.Then,select the appropriate hardware and software to build a physical platform for the magnetic levitation ball control system,according to the characteristics of the system,an iterative learning control algorithm is proposed to design the controller;the mathematical analysis and simulation experiments are combined to determine the specific form and parameters of the iterative learning law;finally,the real-time control experiment is performed on the physical platform,and compared the performance index of the learning control system with that of the classic PID control and that of discrete sliding film control,the results prove that the iterative learning control algorithm proposed in this paper has higher accuracy and stronger robustness in terms of motion control.