Control of an autonomous tracked vehicle and arm using a rule-base reduction based fuzzy expert system

Farchmin, Reid

Permalink

https://hdl.handle.net/2142/115491 Copy

Description

Title

Control of an autonomous tracked vehicle and arm using a rule-base reduction based fuzzy expert system

Author(s)

Farchmin, Reid

Issue Date

2022-04-27

Director of Research (if dissertation) or Advisor (if thesis)

Norris, William R

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Systems & Entrepreneurial Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Center of Mass
• Human Operator Performance Model
• Fuzzy Relations Control Strategy
• Unmanned Ground Vehicle
• Newton-Raphson
• Exponential Forgetting Recursive Least Squares
• Total Least Squares
• Extended Kalman Filter
• Unscented Kalman Filter
• Sliding Mode Observer
• Instantaneous Center of Rotation
• Inertial Measurement Unit
• Inertial Navigation System

Abstract

One of the major deterrents in designing control systems for vehicles with an implement is the inability and difficulty of incorporating human tendencies into the design process of the arm and vehicle controller. In this thesis, the vehicle and implement model, control design, and performance of the control system were investigated. This specific application addressed the design of a 3-actuator electro-hydraulic implement on a tracked electric vehicle. The vehicle and arm models were implemented in the Matlab-Simulink work environment to allow for dynamic and kinematic modeling of the system at near-real time. This allowed for quick feedback on the effectiveness of the controller. A fuzzy expert controller was created for both the base vehicle and the implement which would be placed on top of the tracked vehicle. The fuzzy controllers for the vehicle and the implement were separate at the rule base level but were tied to one another based on key checkpoints and procedures. This ensured system modularity through allowing the user to easily and quickly swap out vehicles or implements based on the task at hand. The fuzzy rule base was then reduced with a hierarchical rule base reduction using the fuzzy relations control strategy (FRCS) as in [1], [2], to lower the number of duplicate or repetitive rules. This ensured the system reacted quicker to a given situation without affecting the performance of the controller. The proof of concept for a reduced rule-base fuzzy expert controller on a tracked vehicle with a bucket implement was proven to work fluidly and effectively.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Reid Farchmin

Owning Collections