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Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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Disturbance observer based adaptive model predictive control for uncalibrated visual servoing in constrained

Zhoujingzi Qiu1, Shiqiang Hu2, Xinwu Liang2

  • 1School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou, China.

ISA Transactions
|September 9, 2020
PubMed
Summary

This study introduces an adaptive model predictive control (MPC) method with a disturbance observer (DOB) to enhance disturbance rejection in image-based visual servoing (IBVS) systems. The adaptive MPC and modified DOB improve system performance under various uncertainties and constraints.

Keywords:
Adaptive model predictive controlDepth-independent interaction matrixDisturbance rejectionModified disturbance observerVisual servoing

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Area of Science:

  • Robotics
  • Control Systems
  • Computer Vision

Background:

  • Image-based visual servoing (IBVS) systems are susceptible to disturbances and parameter uncertainties.
  • Traditional control methods often struggle with unknown system parameters and external disturbances in IBVS.

Purpose of the Study:

  • To develop an adaptive model predictive control (MPC) method integrated with a disturbance observer (DOB) for robust IBVS.
  • To enhance the disturbance rejection capabilities of IBVS systems by addressing unknown parameters and system constraints.

Main Methods:

  • An adaptive MPC controller utilizing an iterative identification algorithm for parameter estimation.
  • A modified disturbance observer (DOB) based on an estimated plant model for feedforward compensation.
  • A depth-independent interaction matrix to handle unknown intrinsic/extrinsic camera parameters and depth.

Main Results:

  • The proposed method effectively improves disturbance rejection performance in IBVS systems.
  • Simulations demonstrate the controller's effectiveness for both eye-in-hand and eye-to-hand camera configurations.
  • The adaptive MPC provides model parameters for both the controller and the modified DOB, optimizing control dynamics.

Conclusions:

  • The adaptive MPC with DOB offers a robust solution for IBVS systems facing uncertainties and disturbances.
  • The developed control scheme successfully manages unknown parameters, system constraints, and external disturbances.
  • This approach enhances the reliability and performance of robotic vision systems.