2026-09-21T13:39:19Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/115491 2023-07-11 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

Norris, William R 2022-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms The student, Reid Farchmin, accepted the attached license on 2022-04-19 at 09:12. The student, Reid Farchmin, submitted this Thesis for approval on 2022-04-19 at 09:35. This Thesis was approved for publication on 2022-04-27 at 16:46. DSpace SAF Submission Ingestion Package generated from Vireo submission #17786 on 2022-11-11 at 13:15:47 Control of an autonomous tracked vehicle and arm using a rule-base reduction based fuzzy expert system Farchmin, Reid 2022-04-27 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 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. Thesis eng https://hdl.handle.net/2142/115491 Copyright 2022 Reid Farchmin M.S. Thesis Systems & Entrepreneurial Engr University of Illinois at Urbana-Champaign Industrial&Enterprise Sys Eng