Predictive Observer-Based Dual-Rate Prescribed Performance Control for Visual Servoing of Robot Manipulators With...

Area of Science:

• Robotics
• Control Systems
• Computer Vision

Background:

• Image-based visual servoing (IBVS) systems face challenges with dual-rate control and field-of-view (FOV) constraints.
• Low camera sampling bandwidth can limit the efficiency of robotic controllers in updating servoing commands.

Purpose of the Study:

• To address dual-rate and view constraints in IBVS for robot manipulators.
• To improve the efficiency and responsiveness of robotic control systems under bandwidth limitations.

Main Methods:

• A predictive observer (PO) was designed to forecast system output during high-level sampling intervals.
• A dual-rate integral-based prescribed performance control (DRIPPC) approach was developed, combining soft-sensing and measured signals.
• The DRIPPC method samples low-frequency state signals while generating high-frequency control actions.

Main Results:

• The proposed control method enables rapid robot manipulator response.
• Strict adherence to field-of-view (FOV) constraints was maintained.
• Effectiveness was validated through experiments on a Universal Robots 5 (UR5) manipulator.

Conclusions:

• The developed dual-rate control strategy effectively manages IBVS challenges.
• The approach enhances robotic system performance while respecting operational constraints.