Adaptive Control of Space Robot System with an Attitude Controlled Base

Abstract

In this report, we discuss adaptive control of a space robot system with an attitude controlled base on which the robot is attached. We at first derive the system kinematic and dynamic equations based on Lagrangian dynamics and linear momentum conversation law. Using the dynamic model developed, we discuss the problem of linear parameterization in terms of dynamic parameters, and have found that in joint space the dynamics can be linearized by a set of combined dynamic parameters, but in inertia space linear parameterization is impossible in general. Then we propose an adaptive control scheme in joint space which has been shown effective and feasible for the cases where unknown or unmodeled dynamics must be considered, such as in tasks of transport an unknown payload, or catching a moving object. The scheme avoids the use of joint acceleration measurement, inversion of inertial matrix, high gain feedback, and considerable computation cost, and at meantime, is also applicable for the fixed-base robot system by slight modification. Since most tasks are specified in inertia space, instead of joint space, we discuss the issues associated to adaptive control in inertia space and identify two potential problems, unavailability of joint trajectory since mapping from inertia space trajectory is dynamic dependent and subject to uncertainty, and nonlinear parameterization in inertia space.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1991
Accession Number
ADA242211

Entities

People

  • Heung-yeung Shum
  • Ju-jang Lee
  • Takeo Kanade
  • Yangsheng Xu

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Space

DTIC Thesaurus Topics

  • Case Studies
  • Computations
  • Control Systems
  • Dynamics
  • Equations
  • Feedback
  • Gain
  • High Gain
  • Inversion
  • Kinetic Energy
  • Linear Momentum
  • Momentum
  • Simulations
  • Space Systems
  • Steady State
  • Trajectories
  • Uncertainty

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aerospace Engineering.

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