Trajectory and Force Control of a Direct Drive Arm.

Abstract

Using the MIT Serial Link Direct Drive Arm as the main experimental device, various issues in trajectory and force control of manipulators were studied in this thesis. Since accurate modelling is important for any controller, issues of estimating the dynamic model of a manipulator and its load were addressed first. Practical and effective algorithms were developed from the Newton-Euler equations to estimate the inertial parameters of manipulator rigid-body loads and links. Load estimation was implemented both on a PUMA 600 robot and on the MIT Serial Link Direct Drive Arm. With the link estimation algorithm, the inertial parameters of the direct drive arm were obtained. For both load and link estimation results, the estimated parameters are good models of the actual system for control purposes since torques and forces can be predicted accurately from these estimated parameters. The estimated model of the direct drive arm was then used to evaluate trajectory following performance by feedforward and computed torque control algorithms. The experimental evaluations showed that the dynamic compensation can greatly improve trajectory following accuracy.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1986
Accession Number
ADA174405

Entities

People

  • Chae H. An

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Amplifiers
  • Artificial Intelligence
  • Closed Loop Systems
  • Cognitive Science
  • Computer Science
  • Control Systems
  • Electrical Engineering
  • Equations
  • Euler Equations
  • Jet Propulsion
  • Measurement
  • Mechanical Engineering
  • Mechanical Properties
  • Strain Gages
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Robotics and Automation.

Technology Areas

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