Multiple Model-Based Robot Control: Development and Initial Evaluation

Abstract

A new form of adaptive model-based robot control has been developed and experimentally evaluated. The Multiple Model Based Control (MMBC) technique utilizes knowledge of nominal manipulator dynamics and principles of Bayesian estimation to provide payload-independent trajectory tracking accuracy. The MMBC is formed by augmenting a model-based controller, which employs feedforward dynamic compensation and constant gain PD feedback, with a payload estimate provided by a Multiple Model Adaptive Estimator. Extensive simulation studies demonstrated the MMBC's ability to adapt to variations in manipulator payload quickly and accurately. Initial experimental evaluations on the first three links of a PUMA-560 validated the algorithm's potential. Keywords: Algorithms, Case studies, Theses, Closed loop parameter estimation.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA205725

Entities

People

  • Larry D. Tellman

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Case Studies
  • Computational Science
  • Computer Programs
  • Computers
  • Control Systems
  • Differential Equations
  • Electrical Engineering
  • Equations Of Motion
  • Estimators
  • Kalman Filters
  • Mathematical Filters
  • Probability
  • Real Variables
  • Resonant Frequency
  • Simulations

Readers

  • Regression Analysis.
  • Robotics and Automation.
  • Tactical Satellite Communications Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy
  • Autonomy - Autonomous System Control