An iterative learning method for realizing accurate dynamic feedforward control of an industrial hybrid robot

Feedforward control based on an accurate dynamic model is an effective approach to reduce the dynamic effect of the robot and improve its performance. However, due to the complicated work environment with considerable uncertainty, it is difficult to obtain a high-precision dynamic model of the robot, which severely deteriorates the achievable control performance. This paper proposes an iterative learning method to accurately design the industrial feedforward controller and compensate for the external uncertain dynamic load of the robot. Based on a standard dynamic model, a complete linear feedforward controller is presented.An iterative design strategy is given to iteratively update the feedforward controller by combining the Moore-Penrose Inverse and the PID learning rate. Experiments are carried out on a 5-DOF industrial hybrid robot to validate the effectiveness of the proposed iterative learning method. The experiment results illustrate that the industrial feedforward controller can rapidly converge to the optimal controller and significantly improve the servo performance by using the proposed method. This paper provides an effective method for applying iterative learning control to an unopened industrial control system. It is very useful for the practical control of hybrid robots in industrial field.