Study of Neuro-Controllers for Motion Control Systems with Distributed Mechanical Flexibility.

Abstract

Control of motion systems involving distributed mechanical flexibility is studied using artificial neural networks. Infinite dimensional nature of the problem due to distributed flexibility, nonlinear dynamics of mechanical structural systems, and fault tolerant operation requirements are taken into consideration. Three different neuro controller architectures are studied: 1) Hopfield nets for modal parameter estimation and real time solution of LQ optimal control problem, 2) Feedforward nets using EKF learning algorithm as a fast learning, trainable nonlinear adaptive controller, 3) CMAC neural network controller for high precision motion control. Thre results are summarized and details are presented in refereed publications.

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Document Details

Document Type
Technical Report
Publication Date
Jan 03, 1994
Accession Number
ADA320001

Entities

People

  • Sabri Cetinkunt

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Algorithms
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computer Programming
  • Computers
  • Control Systems
  • Dynamics
  • Engineering
  • Machine Tools
  • Mechanical Engineering
  • Neural Networks
  • Nonlinear Dynamics
  • Precision
  • Resilience
  • Tools

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Control Systems Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks