Abstract: The automatic joint of broken yarn in ring spinning process has always been a difficult problem in the industry. The low spinning strength is easy to break, and the yarn tension is significantly affected by environmental factors, which makes it difficult to control the yarn tension in the robot joint process. In order to solve the problem of yarn tension control in the process of robot piecing, a neural network adaptive admittance control method based on interactive force prediction is proposed. Firstly, a neural network adaptive adjustment strategy of admittance controller parameters is designed to solve the problem that the dynamic change of the parameters of the joint process environment model leads to the poor force tracking effect of the constant admittance controller. Secondly, in order to solve the problem of the sudden change of error caused by the control lag when the existing adaptive controller tracks the time-varying expected force, an interactive force prediction method is proposed, The parameters of the admittance controller are adjusted in advance by adding the predicted value of the interaction force in the future control period, so as to avoid the large force tracking error when the expected force is abruptly changed; Finally, the simulation test is carried out, the results show that the neural network adaptive controller has good robustness in the force tracking task in dynamic environment. The maximum error and overall error of the neural network adaptive controller based on interactive force prediction in the time-varying expected force tracking task under dynamic environment are reduced by 78.3% and 29.7% respectively compared with those without interactive force prediction, which proves the reliability of the proposed method in the yarn tension tracking control in the process of robot joint.

Key words: ring spinning joint robot, admittance control, neural network adaptive, interaction force prediction