Robust Control of an off Road Single Wheel Module Using Sliding Mode Control and Fuzzy Logic Corrector

Abstract

In this paper, the linear speed tracking control problem of a single-wheel module (SWM) operating in an offroad environment is discussed. The approach is an extension to the authors previous work for angular speed control based on sliding mode control methodology. The linear speed tracking is performed by incorporating an adjusted reference angular speed. This reference speed is constructed utilizing a proportional control method. Further, two different approaches for tire slippage suppression are proposed. Both methods provide a corrected reference angular speed such that tracking it limits the tire slippage growth. The first method utilizes a predefined slippage limit, while the second method bounds the slippage by considering a range of characteristic slippages corresponding to different tire-terrain attributes. The second method is based on fuzzy logic control and specifically is designed according to Mamdani fuzzy inference system and is called the fuzzy logic corrector in this paper. The efficacy of the controlled system is evaluated through numerical simulations. It is shown that the system is able to robustly track the reference linear speed while it suppresses the tire slippage.

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Document Details

Document Type
Technical Report
Publication Date
Sep 29, 2021
Accession Number
AD1149086

Entities

People

  • Amandeep Singh
  • David Gorsich
  • Jesse Paldan
  • Jill Goryca
  • Lee Moradi
  • Masood Ghasemi
  • Michael W. Cole
  • Vladimir Vantsevich

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Closed Loop Systems
  • Control Systems
  • Control Systems Engineering
  • Control Theory
  • Dc Motors
  • Dynamics
  • Efficiency
  • Electric Motors
  • Electric Vehicles
  • Energy Efficiency
  • Engineering
  • Environment
  • Fuzzy Logic
  • Maneuverability
  • Motors
  • Surfaces
  • Vehicles

Readers

  • Control Systems Engineering.
  • Neural Network Machine Learning.
  • Pavement Materials Engineering.

Technology Areas

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