Research On The Double-joint High-precision Motion Control Method Of A Kind Of Truck-mounted Manipulator

As the jewel on the crown of modern manufacturing,industrial manipulator has always been a research hotspot in the field of control owing to its high-precision motion control.This thesis is supported by the National Natural Science Foundation of China.It aims to research the problems of inaccurate modeling,unknown external interference,dead-zone and saturation of actuators,and state constraints in the tracking and control process of industrial manipulator.This article takes the manipulator system in the truck crane as the research object.The main research content can be divided into the following aspects:1.The overall scheme design and modeling of the truck-mounted crane manipulator system.According to the proposed performance indicators and technical requirements,the overall scheme of the truck-mounted crane manipulator system is designed,and the relevant motion parameters of the manipulator are given to provide a foundation for the subsequent algorithm simulation.Aiming at the manipulator system,the Lagrangian method is used to establish its dynamic model,and the motor model is combined to establish the system's hybrid dynamic model.2.Research on high-precision nonlinear motion control algorithm of manipulator.Based on the hybrid dynamics model of the manipulator,the control strategy is designed according to the inaccurate model parameters,time-varying parameters and external interference in the system model,which not only improves the motion control accuracy of the manipulator,but also adapts to a variety of uncertainties that exist in the system.3.Research on the motion control algorithm of the manipulator for the input constraints of the actuator.Considering the non-linear effects of dead-zone and saturation in the motor actuator,basing on the unified modeling of dead-zone and saturation,a static neural network compensator is designed to compensate for all the nonlinearities introduced by the actuator,while another neural network directly estimates the model without using the nominal value parameters of the system.4.Research on manipulator motion control algorithm considering full state constraints.Aiming at the problem of full state constraints of the manipulator system,the position state constraint and speed state constraint of the system are realized by introducing the obstacle Lyapunov function into the backstepping control.Compared with the unconstrained controller,it not only improves the system control accuracy and the speed of convergence,while also reducing the gain of the controller.5.ADAMS/Simulink co-simulation research.By importing the 3D model in Solid Works into Adams,Adams is used to establish the rigid body dynamics model of the truck-mounted crane manipulator system.Co-simulation with ADAMS/Simulink as the simulation environment verifies the effectiveness of the control strategy designed in this dissertation.