Robust Feedforward/Feedback Control Logic for a Target-Tracking Mechanical Arm.

Abstract

An analytic design study is conducted to demonstrate circumstances under which the inclusion of feedforward compensation in a target-tracking control scheme can be expected to offer significant performance gain. In particular, a target-tracking controller design problem for a mechanical arm is developed to assess quantitatively the capacity of feedforward to provide a quicker, more accurate tracking response over wide ranges of uncertainty or variability in the dynamic parameters of both plant and target. The Stanford Aeronautics and Astronautics Department Robotics Lab two-link, two-actuator mechanical arm, inherently a system with variable kinematic and dynamic parameters, provides an appropriate framework for this study. Using recent developments in the theory of quadratic synthesis of robust, low-order optimal controllers, control logic is developed - both with and without feedforward - that enables the arm end point to track a physical target characterized in part by periodic motion of variables or uncertain frequency and phase. It is shown that, using relatively noise-free measurements of target position coordinates only, feedforward compensation can be expected to provide substantial reductions in tracking errors for given constraints on control effort, particularly when the range of variation in target frequency is large. Key words include: tracking, mathematical models.

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 1984
Accession Number
ADA150512

Entities

People

  • B. E. Gardner
  • R. H. Cannon Jr.

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Aeronautics
  • Air Force
  • Algorithms
  • Amplitude
  • Astronautics
  • Classification
  • Computer Programs
  • Control Systems
  • Intensity
  • Linear Programming
  • Plastic Explosives
  • Security
  • Steady State
  • Target Tracking
  • Universities
  • White Noise

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Robotics and Automation.
  • Sensor Fusion and Tracking Systems.

Technology Areas

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