Predictive Observer-Based Dual-Rate Prescribed Performance Control for Visual Servoing of Robot Manipulators With View Constraints
Qifang Liu, Jianliang Mao, Linyan Han, Chuanlin Zhang, Jun Yang
Abstract
This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling bandwidth of the camera, potentially diminishing the efficiency of the robotic controller in updating low-level servoing control commands, a predictive observer (PO) is initially designed to forecast the system output during the high-level sampling intervals. Moreover, by leveraging a mixture of soft-sensing and real-measured signals, a dual-rate integral-based prescribed performance control (DRIPPC) approach is devised. The benefit lies in that the proposed control method samples the low-frequency state signal while generating a relatively high-frequency control action, ensuring rapid response of the robot manipulator while maintaining strict adherence to field-of-view (FOV) constraints. Finally, the effectiveness of the proposed control approach is validated through a series of experiments conducted on a Universal Robots 5 (UR5) manipulator.