Optimal adaptive control with ΘLQG

This thesis introduces ΘLQG, the hyper extension of the Linear Quadratic Gaussian Regulator, for solving general purpose (locally optimal) nonlinear adaptive control on multiple-input multiple-output systems. This approach can be used to stabilize an under-actuated under-observed nonlinear dynamic system with noisy measurements while simultaneously identifying system parameters. It requires a parameterized dynamic model with operation about equilibrium points. Its general construction can also be used in disturbance rejection, gain scheduling and reference tracking. Specific case examples are introduced.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Wendt, Luke

Contributors dc:contributor

• Levinson, Stephen E.

• Optimal Adaptive Control
• Multiple-Input Multiple-Output (MIMO)
• Extended Kalman Filter (EKF)
• Hyper-Extended Kalman Filter (EKF)
• Linear Quadratic Regulator (LQR)
• Linear Quadratic Gaussian Regulator (LQG)
• Hyper Extended LQG (ΘLQG)
• Pendulum on Cart

Statement dc:rights

• Copyright 2013 Luke Adam Wendt

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/44105

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/44105