Adaptive neural synchronized impedance control for cooperative manipulators processing under uncertain environments. (June 2022) - Zhai, Anbang; Zhang, Haiyun; Wang, Jin; Lu, Guodong; Li, Junjie; Chen, Silu
Highlights: In position tracking loop, the RBF-neural network is applied to approximate the inaccurate dynamic and external disturbance of the cooperative manipulators system. Compared with the traditional neural network updating law, the proposed weighted square neural network updating law can efficiently accelerate the convergence speed and reduce the steady-error meanwhile. The synchronization factor is introduced to the design of global coupled sliding-mode error and deal with the synchronization position-force tracking problem between the cooperative robotic manipulators. By adjusting and increasing the synchronization factor, the synchronization tracking error is gradually reduced. In force tracking loop, this paper creatively utilizes the RBF-neural network estimation term to reform a new impedance control model. Serial experiments with five Cases have proved that the proposed ANSIC method can quickly and accurately track the desired grinding force under the uncertain surface curve and stiffness of the workpiece. 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.