Multi-axis Synchronous Control Technology For Industrial Robot

In particular,industrial robots refer to manipulators with multiple degrees of freedom,which are suitable for repetitive and highly demanding industrial production operations.They are often used in production lines such as spraying,welding and assembly,replacing higher-cost labor.It also increases production efficiency and yield.First of all,this article mainly analyzes the principle of industrial robots,and models them.The Lagrange method is used to establish the dynamical model equation of the robots as the controlled object in the paper.In addition,a cross-coupling control strategy is proposed for the problem of multi-axis synchronization to achieve simultaneous tracking of position and velocity between multiple axes.Then,an adaptive control algorithm is proposed for multi-axis synchronous control of industrial robots.The advantage of adaptive algorithms is the online estimation of unknown parameters in the system.Model reference adaptive algorithm is widely used,using the system model as a reference,the unknown parameters in the system ate extracted as variables,the reference model of the system is linearized,and the unknown online estimation parameters are added to the control law.Combining the adaptive control algorithm with the neural network,without relying on the system reference model,the RBF neural network is used to approximate all the models instead of the original unknown parameters,which improves the system's ability to overcome friction and external interference.Then,it studies the application of sliding mode variable structure algorithm to industrial robots.It mainly solves the nonlinear characteristics and unpredictable external interference problems of the system.Once the system enters the sliding mode motion state,it has nothing to do with the control object parameter change and system disturbance.It has strong robustness,but it inevitably causes the system's chattering phenomenon.For this purpose,the law of exponential approaching laws is used to constrain the trajectory of the sliding mode to reduce buffeting.In addition,different stages of the sliding mode movement require different gains,and the gains of the fuzzy system and the adaptive control adjustment system are introduced to effectively reduce chattering.Finally,an experimental platform composed of upper computer,servo driver and industrial robot was established.The upper computer and servo driver used Ether CAT communication,and established a dynamic model for the EFORT industrial robot in the laboratory.The next,the obtained kinetic model equations are simulated in Matlab and each algorithm Simulink,and the output control torque data is obtained.A real-time task is established on the upper computer's Ether CAT master station,and the output control torque data is periodically transmitted to the servo driver through the robot control driver program to control the industrial robot to track the desired trajectory.