Research On Torque Prediction,Load Identification And Collision Detection Of Industrial Robot Based On Neural Network

The control system of industrial robots has the characteristics of time variation,strong coupling and non-linearity,and also contains many uncertain factors such as measurement error,random disturbance and variable load,so it is quite difficult to obtain an accurate motion control model.In order to improve the ability of the control model to deal with the above uncertainties,the joint torque prediction and terminal load identification of the robot are studied in this paper by using the strong nonlinear fitting ability of the neural network.For the prediction of robot joint torque,firstly,under the condition of fixed load,the robot working scenes are divided into path fixed speed fixed,path fixed speed variable,path and speed variable,etc.,and the corresponding joint torque prediction model is established respectively.The prediction absolute error with variable path and speed is 3.9%,and the prediction absolute error of other scenarios is around 1%.In the case of fixed path,it is verified that the speed and load have little influence on the prediction effect of the model.The torque prediction model that can adapt to the change of load is established for the scenarios of path fixed speed and path fixed speed variable,and the corresponding absolute error of prediction is 4.47% and 1.42%,respectively.For the terminal load identification of the robot,load identification models corresponding to five scenarios were established,and the absolute error of load prediction was 2.22%,2.88%,5.55%,3.44% and 21.6%,respectively.Under the condition of fixed path and speed,the smaller the speed,the better the prediction effect.Aiming at robot collision detection,the effects of two methods based on fixed threshold and neural network are compared and analyzed.The experimental results show that the neural network algorithm is accurate in predicting the joint torque and end load of most robot scenes and can be applied to robot control.However,the prediction effect of some complex scenes is slightly worse,which still needs further research and analysis.The neural network-based method can accurately detect the collision location and the detection delay is less than8 ms.