Research On Modeling And Control Method Of Three-Degree-of-Freedom Hoisting Manipulator

In today's rapidly changing science and technology,manipulators have been applied in many industrial occasions.As a large-scale manipulator,hoisting manipulators have been widely used in factories,docks and other large load occasions.With the increasing demand for hoisting manipulators,the performance requirements for hoisting manipulators are also gradually increasing,so the research on the control of hoisting manipulators is of great practical significance.This paper mainly focuses on the following research on the hoisting manipulator:First,according to the actual structure of the hoisting manipulator,it is simplified as a three-degree-of-freedom manipulator,in which joints 1 and 3 are rotary joints,and joint 2is a telescopic joint.Considering the elastic deformation of the telescopic link under the action of a large load,the accuracy and stability of the hoisting will be affected.Therefore,it is simplified to the Euler-Bernoulli beam model,and its elastic deformation is described by the assumed mode method.The pose transformation matrix of the hoisting manipulator is established,and then the dynamic model of the hoisting manipulator is established according to the Lagrange method,and the simulation verification is carried out with the model established in ADAMS based on the parameters of the manipulator's link,which verifies the accuracy of the hoisting manipulator model.Secondly,based on the dynamic model of the hoisting manipulator established by the above method,the trajectory tracking control is carried out in the case of considering the external disturbance,and the whole-process nonlinear integral sliding mode method is used to design the controller.The controller accelerates the process from the initial state to the sliding mode surface,so that the whole trajectory tracking is on the sliding mode surface,so that it still has good performance when the transient process is disturbed and parameter changes.It has the property of "small error amplification,large error saturation",which improves the transient performance without changing the steady-state error.The algorithm designed in this chapter,the traditional integral sliding mode algorithm and the nonlinear integral sliding mode algorithm are used for simulation experiments under the conditions of small initial error and large initial error,respectively,to verify the effectiveness of the algorithm.Finally,in order to suppress the vibration of the manipulator,a piezoelectric actuator is selected as the actuator for vibration suppression of the manipulator.The state equation of the connecting rod 2 based on the action of the piezoelectric actuator is established,and the minimum energy consumption and maximum energy transfer are taken as the goals,and the installation position of the piezoelectric actuator is optimized by using the genetic algorithm.The average value of the positions is taken as the installation position of the piezoelectric actuator.Based on the dynamic model of the manipulator under the action of the actuator,it is decomposed into a slow-changing subsystem and a fast-changing subsystem using the singular perturbation principle.For the slow-changing subsystem,the RBF neural network is used to approach the compensation method for modeling uncertainty and external disturbance,combined with the whole-process nonlinear integral control,and the trajectory tracking control of the manipulator is carried out.For the fast-changing subsystem,the robust H-infinity controller is used to suppress the vibration.The simulation based on the combination of the two controllers shows that the addition of the RBF neural network can reduce the tracking error and the chattering of the sliding surface.The addition of the robust H-infinity control can suppress the vibration of the manipulator and make the manipulator run.more stable.