{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110585"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110585","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Steering control and Kalman filter position estimation comparison for an autonomous underwater vehicle","abstract":"Autonomous vehicles for sub-sea exploration are gaining in popularity. They offer longer operational time, can reach a wider and deeper area of the sea with low risk of failure. The control system and the localization system are two of the most important components that ensure the success of the mission. However, the performance of these subsystems is affected by external noise and disturbances. This thesis presents a Hierarchical rule-based reduction fuzzy controller as a solution to control systems suffering from noisy feedback and affected by external current flow disturbances. Performance comparisons with LQR and Pure Pursuit controllers show that under these conditions, the hierarchical rule-base reduction fuzzy logic controller is able to reject disturbances and sensor noises better than its counterparts. Furthermore, this research observes the performance of all three controllers under challenging path trajectories. As the complexity of the path increased, the LQR controller's performance was observed to be better than that of Fuzzy and Pure Pursuit controllers. It is suggested under uncertain dynamics and noisy sensor conditions, a fuzzy controller should be used because of its higher ability to filter out noises and reject disturbances. Challenges in localization are addressed using the Unscented version of the Kalman filter, in which reduced order dynamic model predictions are fused with measurements. When compared to the Extended Kalman filter, the Unscented Kalman Filter was observed to suppress noise much better; its performance was observed to be robust as the noise in sensor data increased. The EKF was observed to have a lower error covariance matrix value than the UKF, suggesting higher confidence in the EKF value. The UKF values were well within the acceptable limits.","abstract_html":"Autonomous vehicles for sub-sea exploration are gaining in popularity. They offer longer operational time, can reach a wider and deeper area of the sea with low risk of failure. The control system and the localization system are two of the most important components that ensure the success of the mission. However, the performance of these subsystems is affected by external noise and disturbances. This thesis presents a Hierarchical rule-based reduction fuzzy controller as a solution to control systems suffering from noisy feedback and affected by external current flow disturbances. Performance comparisons with LQR and Pure Pursuit controllers show that under these conditions, the hierarchical rule-base reduction fuzzy logic controller is able to reject disturbances and sensor noises better than its counterparts. Furthermore, this research observes the performance of all three controllers under challenging path trajectories. As the complexity of the path increased, the LQR controller's performance was observed to be better than that of Fuzzy and Pure Pursuit controllers. It is suggested under uncertain dynamics and noisy sensor conditions, a fuzzy controller should be used because of its higher ability to filter out noises and reject disturbances. Challenges in localization are addressed using the Unscented version of the Kalman filter, in which reduced order dynamic model predictions are fused with measurements. When compared to the Extended Kalman filter, the Unscented Kalman Filter was observed to suppress noise much better; its performance was observed to be robust as the noise in sensor data increased. The EKF was observed to have a lower error covariance matrix value than the UKF, suggesting higher confidence in the EKF value. The UKF values were well within the acceptable limits.","abstract_has_math":false,"creators":["Rajput, Ayush"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Norris, William R"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:13:30Z","date_published":"2021-09-17T01:13:30Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Kalman Filter","Unscented Kalman Filter","Localization","Control, Fuzzy Controller","LQR Controller","Pure Pursuit Controller","Autonomous Vehicle","Underwater Vehicle","Hierarchical Rule Base Reduction"],"languages":["en"],"rights":["Copyright 2021 Ayush Rajput"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110585","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Norris, William R"]},{"key":"dc:creator","label":"Author","values":["Rajput, Ayush"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:13:30Z","2021-04-28","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Kalman Filter","Unscented Kalman Filter","Localization","Control, Fuzzy Controller","LQR Controller","Pure Pursuit Controller","Autonomous Vehicle","Underwater Vehicle","Hierarchical Rule Base Reduction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Ayush Rajput"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110585"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Autonomous vehicles for sub-sea exploration are gaining in popularity. They offer longer operational time, can reach a wider and deeper area of the sea with low risk of failure. The control system and the localization system are two of the most important components that ensure the success of the mission. However, the performance of these subsystems is affected by external noise and disturbances. This thesis presents a Hierarchical rule-based reduction fuzzy controller as a solution to control systems suffering from noisy feedback and affected by external current flow disturbances. Performance comparisons with LQR and Pure Pursuit controllers show that under these conditions, the hierarchical rule-base reduction fuzzy logic controller is able to reject disturbances and sensor noises better than its counterparts. Furthermore, this research observes the performance of all three controllers under challenging path trajectories. As the complexity of the path increased, the LQR controller's performance was observed to be better than that of Fuzzy and Pure Pursuit controllers. It is suggested under uncertain dynamics and noisy sensor conditions, a fuzzy controller should be used because of its higher ability to filter out noises and reject disturbances. Challenges in localization are addressed using the Unscented version of the Kalman filter, in which reduced order dynamic model predictions are fused with measurements. When compared to the Extended Kalman filter, the Unscented Kalman Filter was observed to suppress noise much better; its performance was observed to be robust as the noise in sensor data increased. The EKF was observed to have a lower error covariance matrix value than the UKF, suggesting higher confidence in the EKF value. The UKF values were well within the acceptable limits.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Ayush Rajput, accepted the attached license on 2021-04-27 at 09:31.","The student, Ayush Rajput, submitted this Thesis for approval on 2021-04-27 at 09:46.","This Thesis was approved for publication on 2021-04-28 at 16:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16579 on 2021-09-16 at 16:48:35","Made available in DSpace on 2021-09-17T01:13:30Z (GMT). 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However, the performance of these subsystems is affected by external noise and disturbances. This thesis presents a Hierarchical rule-based reduction fuzzy controller as a solution to control systems suffering from noisy feedback and affected by external current flow disturbances. Performance comparisons with LQR and Pure Pursuit controllers show that under these conditions, the hierarchical rule-base reduction fuzzy logic controller is able to reject disturbances and sensor noises better than its counterparts. Furthermore, this research observes the performance of all three controllers under challenging path trajectories. As the complexity of the path increased, the LQR controller's performance was observed to be better than that of Fuzzy and Pure Pursuit controllers. It is suggested under uncertain dynamics and noisy sensor conditions, a fuzzy controller should be used because of its higher ability to filter out noises and reject disturbances. Challenges in localization are addressed using the Unscented version of the Kalman filter, in which reduced order dynamic model predictions are fused with measurements. When compared to the Extended Kalman filter, the Unscented Kalman Filter was observed to suppress noise much better; its performance was observed to be robust as the noise in sensor data increased. The EKF was observed to have a lower error covariance matrix value than the UKF, suggesting higher confidence in the EKF value. The UKF values were well within the acceptable limits.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, Ayush Rajput, accepted the attached license on 2021-04-27 at 09:31.","The student, Ayush Rajput, submitted this Thesis for approval on 2021-04-27 at 09:46.","This Thesis was approved for publication on 2021-04-28 at 16:45.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16579 on 2021-09-16 at 16:48:35","Made available in DSpace on 2021-09-17T01:13:30Z (GMT). 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