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Optimal DMD Koopman Data-Driven Control of a Worm Robot.

Mehran Rahmani1, Sangram Redkar1

  • 1The Polytechnic School, Ira Fulton School of Engineering, Arizona State University, Mesa, AZ 85212, USA.

Biomimetics (Basel, Switzerland)
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal data-driven controller for bio-inspired worm robots. It uses Koopman theory and dynamic mode decomposition to linearize the robot

Keywords:
DMD methodKoopman theoryLQRdata-drivenworm robot

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Area of Science:

  • Robotics
  • Control Systems
  • Bio-inspired Engineering

Background:

  • Bio-inspired robots mimic natural systems, with worm robots offering potential in medicine and rescue.
  • Controlling worm robots is complex due to nonlinear dynamics and external noise.
  • Existing control methods struggle with the inherent complexities of these robots.

Purpose of the Study:

  • To develop an optimal data-driven controller for enhanced worm robot maneuverability.
  • To address the challenges posed by nonlinear dynamics and external disturbances in worm robot control.
  • To validate the efficacy of a novel control strategy through simulation.

Main Methods:

  • Data acquisition from the nonlinear dynamic model of the worm robot.
  • Application of Koopman theory to derive a linear dynamic model.
  • Utilizing Dynamic Mode Decomposition (DMD) to compute the Koopman operator.
  • Implementation of a Linear Quadratic Regulator (LQR) for control.

Main Results:

  • Successful linearization of the worm robot's nonlinear dynamics using Koopman theory.
  • Effective generation of the Koopman operator via Dynamic Mode Decomposition.
  • Demonstrated performance of the Linear Quadratic Regulator (LQR) controller in simulations.
  • Verification of the proposed data-driven control method's efficacy.

Conclusions:

  • The proposed optimal data-driven controller effectively manages worm robot dynamics.
  • Koopman theory and DMD provide a robust framework for linearizing complex robotic systems.
  • The LQR controller ensures stable and accurate control, paving the way for practical applications.