An Empirical Vehicle Speed Model for Tuning Throttle Controller Parameters

Abstract

Controlling the speed of an autonomous ground vehicle is a necessity for autonomous driving. Proportional-integral-differential (PID) controllers are a common solution for controlling the throttle setting of a vehicle in order to achieve a desired speed. PID controllers feature three free parameters which must be "tuned" to achieve the desired controller behavior. While many theoretical approaches for automatic tuning of PID parameters have been considered, practical applications still require extensive field testing because even detailed physical models of ground vehicles often fail to adequately capture all the effects that influence vehicle speed. In order to facilitate tuning of PID parameters for a real-world vehicle, in this work a fully empirical model of vehicle longitudinal dynamics is proposed. With a short series of measurements, a predictive model of the vehicle speed can be developed by fitting the model to the measured data. The empirical model presented in this work has the advantages that it is simple - it does not require any detailed measurements of the vehicle properties but is rather easily fit to real measurements - and flexible - it can be used for a variety of vehicles and conditions. In this work, the development of the model is outlined, and an application of the model is shown for two different vehicles, the Polaris MRZR4 and the Clearpath Warthog. The applicability of the empirical model is demonstrated by tuning and testing a real PID controller for the MRZR4.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2022
Accession Number
AD1166735

Entities

People

  • Christopher Goodin
  • Christopher H. Hudson
  • Daniel W. Carruth
  • Lucas D. Cagle
  • Marc N. Moore
  • Paramsothy Jayakumar

Organizations

  • Mississippi State University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Air Resistance
  • Algorithms
  • Computer Science
  • Control Systems
  • Control Systems Engineering
  • Data Acquisition
  • Electric Vehicles
  • Engineering
  • Engineers
  • Equations
  • Ground Vehicles
  • Information Science
  • Kalman Filters
  • Mechanical Engineering
  • Neural Networks
  • Predictive Modeling
  • Simulations
  • Statistical Analysis
  • Steady State

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Modeling and Simulation
  • Robotics and Automation.