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Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot.

Dongliang Wang1, Yong Gao2, Wu Wei2

  • 1School of Department of Electronic and Information Engineering, Shantou University, 515063, Guangdong, China; Key Lab of Digital Signal and Image Processing of Guangdong Province, Shantou University, 515063, Guangdong, China.

ISA Transactions
|June 30, 2024
PubMed
Summary

A new adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) enhances Mecanum-wheeled mobile robot (MWMR) trajectory tracking. This robust method ensures precise control despite external disturbances and model uncertainties.

Keywords:
Mecanum-wheeled mobile robotModel predictive controlSliding mode observerTrajectory tracking

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Area of Science:

  • Robotics and Control Systems
  • Mechatronics
  • Applied Mathematics

Background:

  • Mecanum-wheeled mobile robots (MWMRs) face challenges in trajectory tracking due to external disturbances and model uncertainties.
  • Existing control methods may struggle to maintain precision and adhere to physical constraints under dynamic operating conditions.

Purpose of the Study:

  • To develop a novel control strategy for precise trajectory tracking of MWMRs.
  • To enhance the robot's robustness against external disturbances and internal model uncertainties.
  • To ensure that physical constraints of the MWMR are respected during operation.

Main Methods:

  • Design of a model predictive controller (MPC) based on the nominal MWMR dynamics, transforming trajectory tracking into a constrained quadratic programming (QP) problem.
  • Integration of an adaptive variable power sliding mode observer (AVPSMO) as a feedforward compensator to mitigate disturbances and uncertainties.
  • Mathematical proof of the AVPSMO's stability using Lyapunov theory.

Main Results:

  • The proposed AVPSMO-MPC method achieves precise trajectory tracking for MWMRs.
  • The control strategy effectively suppresses the impact of external disturbances and model uncertainties.
  • Simulations demonstrate the method's effectiveness and robustness, confirming constraint satisfaction.

Conclusions:

  • The AVPSMO-MPC method offers a robust and effective solution for MWMR trajectory tracking.
  • This approach enhances control precision and reliability in the presence of dynamic uncertainties.
  • The study validates the proposed method's performance through comparative simulations.