Pneumatic Muscle Actuator Control

Abstract

This research is concerned with investigating methods for the control of McKibben pneumatic actuators, or pneumatic muscles (PMs). PMs are a novel type of actuator that closely mimic human skeletal muscles in size and power capabilities. PMs are considered by the Air Force for use in exoskeletons to be worn by humans for strength augmentation and for use as actuators in robotic systems. In this research, we investigate adaptive, sliding mode, and soft computing approaches to control of PMs and robotic systems actuated by PMs. The soft computing approaches include neuro-fuzzy modeling of an actual PM in the Human Effectiveness Lab at Wright Patterson Air Force Base, and evolutionary design of a fuzzy PID controller based on this model. We also investigate a type of MIMO fuzzy model predictive control for a planar arm actuated by four PMs. Some of the controllers are tested on the actual PM at WPAFB while others are proven in simulations. A byproduct of this research is an evolutionary fuzzy training algorithm useful for identification of dynamical systems as well as classification problems.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2004
Accession Number
ADA420339

Entities

People

  • John H. Lilly

Organizations

  • University of Louisville

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Actuators
  • Adaptive Systems
  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computers
  • Control Surfaces
  • Control Systems
  • Differential Equations
  • Health Services
  • Joints (Anatomy)
  • Machine Learning
  • Mathematical Models
  • Medical Personnel
  • Model Predictive Control
  • Neural Networks

Readers

  • Defense Acquisition Program Management
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - Autonomous System Control