Nonparametric Modeling and Control of High Performance Maneuvers

Abstract

We developed several nonparametric modeling techniques as well as nonlinear controller design techniques. We successfully implementing these techniques on a robot arm. We used a locally weighted modeling approach as the basis of our nonparametric modeling technique. Locally weighted modeling avoids negative interference by retaining the original training data, so the approach is adaptive to changing data distributions. We used sophisticated representations of high dimensional sub-manifolds to enable dynamic programming in higher dimensional spaces than are currently possible.

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Document Details

Document Type
Technical Report
Publication Date
Jan 20, 1997
Accession Number
ADA329721

Entities

People

  • Christopher G. Atkeson

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computer Programming
  • Control Systems
  • Dynamic Programming
  • Information Processing
  • Information Systems
  • Kernel Functions
  • Linear Systems
  • Machine Learning
  • Mathematical Models
  • Models
  • Neural Networks
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Training

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Computational Modeling and Simulation
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers