Position and force hybrid control of robotic manipulator by neural network (adaptive control of 2 DOF manipulators)

A position/force hybrid control of a robotic manipulator based on a neural network model is proposed with consideration of the dynamics of objects and the orientations of the robotic manipulator. This proposed system consists of a standard PID (proportional plus integral plus derivative) controller, the gains of which are augmented and adjusted depending on objects and orientations of manipulators through a process of learning. The proposed method shows better performance than the conventional PID controller, yielding a wider range of applications. It is shown that the proposed controller is applicable to cases of position/force hybrid control of multi-degree-of-freedom manipulators. Simulations and experiments were carried out for the case of two-degree-of-freedom robotic manipulators