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

Qifang Liu, Jianliang Mao, Linyan Han

IEEE Transactions on Cybernetics

|March 14, 2025

Summary

This study introduces a dual-rate control method for robot manipulators using image-based visual servoing (IBVS). The approach enhances control efficiency and ensures field-of-view (FOV) constraints are met.

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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.