Industrial Robot Modeling And Disturbance Rejection Control

In the context of the continuous improvement of the industrialization level,with the increasing aging of population and rising labor costs,the industrial robot industry is developing rapidly.At the same time,higher requirements have been placed on the accuracy and stability of its control algorithms.In view of the complex structure of the industrial robot system,the severe coupling of joints and the influence of various factors such as nonlinear friction and parameter uncertainties,it is difficult for traditional linear control algorithms to obtain satisfactory closed-loop system performance.Therefore,research and design of control algorithms with good robustness and high-precision tracking performance are of great significance to the application of industrial robots.Aiming at the high-precision trajectory tracking problem of a six-degree-of-freedom industrial robot,this paper comprehensively considers two aspects of system modeling and controller design,the dynamic model of the system is obtained through system identification when the dynamic parameters of the robot system are unknown,and advanced control strategies are designed based on sliding mode control and disturbance observe to improve the anti-disturbance ability of the system.The main research contents are as follows:1.The basic concepts and modeling methods of the robot system's kinematics and dynamics are introduced in this article,the D-H parameter method and Lagrangian equation are used to derive the kinematics and dynamics models of the six-degree-of-freedom industrial robot respectively,and the characteristics of the model are analyzed.2.In view of the complexity of the robot dynamics model and it is difficult to obtain an accurate model,parameter identification is adopted to obtain the dynamics model.First,the parameter linearization model of the system based on the theory of dynamic modeling is obtained,then the optimal excitation trajectory is caculated by using the optimization method,and the experimental data is collected and processed.Then,the weighted least square method is selected to identify and calculate the dynamic parameters.Finally,the identified robot dynamic parameters are obtained.3.The nonlinear factors and modeling errors in the robot system are regarded as lumped disturbances and extended as the new state of the system,and the extended state observer is applied to observe and compensate them.At the same time,the identification model is used for feedback linearization,the sliding mode control is adopted as the controller design part,and finally the compound controller of the system is obtained.The tracking error of each joint is proved to converge to zero asymptotically by using the Lyapunov theorem,and the chattering problem of sliding mode control is effectively alleviated.By carrying out simulations and experiments,it is proved that the designed method has better anti-disturbance performance.4.Considering that the extended state observer can only estimate the disturbance with its derivative approaching zero,for the high-order time-varying disturbances in the system,a generalized proportional integral observer is proposed to replace the extended state observer to further improve the trajectory tracking control performances of the robot system.The proposed scheme is compared with the extended state observerbased sliding mode control method through simulations and experiments.The results show that the proposed method can accurately estimate the disturbances and improve the dynamic performances and anti-disturbance ability of the system.