In response to the problems in manipulator trajectory tracking control

including inaccurate dynamic parameters and unknown external disturbances

a control method integrating dynamic feedforward with iterative learning is proposed. Firstly

dynamic feedforward control was employed to enable the manipulator to achieve basic tracking of the desired trajectory

and the control system equations were linearized within a small vicinity of the desired trajectory. Subsequently

an iterative learning controller was introduced to mitigate the impact of inaccuracies in dynamic parameters and external disturbances. Through iterations

it further reduced the trajectory tracking errors. The error convergence of the present control scheme was analyzed by using a compression mapping approach. The simulation experiments were conducted by using MWORKS.Sysplorer platform

and the experiments were performed on a six-axis serial-link manipulator. The results indicated that the present approach

through iterative learning

gradually reduced the trajectory tracking errors of the manipulator.