Fuzzy logic iterative learning control for trajectory tracking of parallel kinematic manipulators

This work presents the application of a fuzzy logic iterative learning controller (FLC-ILC) for the trajectory tracking of Parallel Kinematic Manipulators (PKM). The PKM are increasingly used in repetitive tasks, and in order to benefit from this characteristic, an ILC is introduced and combined with the proportional-derivative (PD) feedback controller to improve the tracking error from iteration to iteration. As the PKM are effected by external disturbances, the fixed controller gains can degrade the performances. To overcome this problem a FLC is developed to tune the gains matrices of the ILC. A high-speed pick and place repetitive trajectory has been tested and external disturbance has been added to the dynamic model. Simulation is carried out on the parallel Delta robot and it has shown the effectiveness and the robustness of the proposed controller.