A Proposed Methodology for the Control of a Semi-Robotic Convoy

Abstract

The purpose of this thesis is to develop a generic control law for unmanned-trail vehicles as they follow a manned lead vehicle. The development of this semi robotic convoy control law begins with a model of an individual vehicle. Two methods are then explored of coupling these into a model of the column. A relationship between these two methods is derived. The model is then expanded to n vehicles. Utilizing a digital simulation, a three-vehicle convoy is controlled in one degree of freedom (DOF) using pole-placement, state- feedback control theory. The analysis shows this to be an unacceptable method of control due to the steady state error. The 1 DOF model is then controlled with series compensation. Simulations verify that the steady-state error is eliminated. The system is then expanded into a 2 DOF system. Using the same series compensator, a 2 DOF simulation is developed. It is shown that the only additional requirement of the 2 DOF system is that the trail vehicles need to determine their orientation. This is accomplished by first saving the position and velocity profile of the lead vehicle and then developing a search algorithm to find the appropriate information. The simulation verifies that the convoy is controlled within the specifications of the system.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1991
Accession Number
ADA231622

Entities

People

  • A. T. Economy Iii

Tags

Communities of Interest

  • Autonomy
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Closed Loop Systems
  • Collision Avoidance
  • Command And Control
  • Computer Simulations
  • Control Systems
  • Control Systems Engineering
  • Difference Equations
  • Differential Equations
  • Dynamic Response
  • Global Positioning Systems
  • Guidance
  • Loran
  • Mechanical Engineering
  • Navigation
  • Radio Navigation
  • United States
  • United States Military Academy

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Robotics and Automation.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

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