Steering control and Kalman filter position estimation comparison for an autonomous underwater vehicle

Rajput, Ayush

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https://hdl.handle.net/2142/110585 Copy

Description

Title

Steering control and Kalman filter position estimation comparison for an autonomous underwater vehicle

Author(s)

Rajput, Ayush

Issue Date

2021-04-28

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

Norris, William R

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Industrial Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Kalman Filter
• Unscented Kalman Filter
• Localization
• Control, Fuzzy Controller
• LQR Controller
• Pure Pursuit Controller
• Autonomous Vehicle
• Underwater Vehicle
• Hierarchical Rule Base Reduction

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.

Graduation Semester

2021-05

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/110585

Copyright and License Information

Copyright 2021 Ayush Rajput

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