Guidance, control and estimation of autonomous vehicle systems

This research focuses on designing practical guidance, control and estimation algorithms for autonomous vehicle systems. The objective is to provide robust control and estimation algorithms for both single and multiple autonomous vehicles under realistic motion, sensing, and communication conditions such as uncertain dynamics, passive sensor, limited communication range, and the complicated coupling among them. With that objective in mind, we start with designing vision-based guidance and estimation algorithms for small unmanned air vehicles (UAVs) to track a ground target. The tracking task is for the UAV to maintain a horizontal orbit around the target with a predefined radius, so as to provide continuous visual surveillance of the target with a desired resolution. We design simple vision-based guidance laws for the cases of both static target and moving target by controlling only the turn rate of the UAV, and give rigorous proofs of the “almost global” asymptotic stability of the closed-loop systems. We extend the tracking algorithm for a single UAV to the case of coordinated target tracking with multiple UAVs by controlling only the turn rates, where, in addition to orbiting about the target, each UAV is required to maintain given phase differences from others. In order to provide continuous estimates of the target’s motion, including its position, velocity, and heading angle, we formulate an estimation problem in a deterministic setup such that the recently developed fast estimator can be applied which yields guaranteed transient performance. The second part of this thesis is dedicated to the topic of distributed control of a group of unmanned vehicles, in the presence of realistic dynamical, sensing and communication constraints. The objective is to drive a group of unmanned vehicles with uncertain dynamics from different initial conditions to aggregate towards a moving target of interest and align their velocities with it, resulting in a moving flock. We develop a cascaded control framework to decouple the inter-agent coordination from local uncertainty compensation for each single agent, such that existing algorithms in literature designed for simple ideal agent kinematics can be used as the outer-loop, while L1 adaptive controllers are used for the inner-loop. Two different ideal agent model are considered, namely, the double integrator and the nonholonomic model. To better handle the uncertainty compensation under limited computation and sensing capability, which is a quite common case for cheap and small autonomous vehicles, we develop a new L1 adaptive controller in the third part of this thesis. It features a modified piecewise constant adaptive law that imposes significantly less stringent requirements on the computation and sensing frequencies. The main idea is to more efficiently exploit the information of the uncertainties from the past samples and use this information to compensate for the uncertainty in the next sample period. We compare the performance and robustness trade-off of the new and the existing L1 adaptive controllers.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Li, Zhiyuan

Contributors dc:contributor

• Hovakimyan, Naira
• Basar, Tamer
• Stipanović, Dušan M.
• Salapaka, Srinivasa M.

• autonomous system
• unmanned system
• distributed control
• Adaptive Control
• cooperative control
• vision based guidance

Statement dc:rights

• Copyright 2013 Zhiyuan Li

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/46871

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/46871