Some computational principles derived from cortical neuroanatomy

Abstract

In robotic cooperation manufacturing occasions, like grinding, assembling, welding, etc., the position-force synchronization tracking control for robotic cooperative manipulators is critical to improve the comprehensive manufacturing quality with high-precision and high-adaptability. In terms of these problems, this paper proposes an adaptive neural synchronized impedance controller (ANSIC) for cooperative manipulators processing. The proposed method includes two non-parallel control loops of the cooperative system to achieve and guarantee the desired movement trajectory and manufacturing force of the cooperation task. In the inner position tracking loop, an adaptive RBF-neural network based synchronization sliding controller is designed to simultaneously estimate the uncertain dynamic parameters of the robotic manipulators and improve the cooperative position tracking precision. In the outer force tracking loop, another RBF-neural network is applied to reform the impedance control model automatically and compensate the position and stiffness errors of the uncertain workpiece environment. Mathematical proof and experiments under various conditions are conducted. The results demonstrate the effective convergences of both the cooperative processing trajectory and force despite the uncertain environments.