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Robust control of electrohydraulic soft robots.
Volchko, Angella; Mitchell, Shane K; Scripps, Tyler G; Turin, Zoe; Humbert, J Sean.
Afiliação
  • Volchko A; Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States.
  • Mitchell SK; Artimus Robotics Inc., Boulder, CO, United States.
  • Scripps TG; Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States.
  • Turin Z; Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States.
  • Humbert JS; Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States.
Front Robot AI ; 11: 1333837, 2024.
Article em En | MEDLINE | ID: mdl-39157793
ABSTRACT
This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize H ∞ ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Robot AI Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Robot AI Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Suíça