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Convex Model Predictive Control for Vehicular Systems


Huang, Tiffany Amy (2014) Convex Model Predictive Control for Vehicular Systems. Senior thesis (Major), California Institute of Technology. doi:10.7907/PNN7-SC35.


In this work, the author presents a method called Convex Model Predictive Control (CMPC) to control systems whose states are elements of the rotation matrices SO(n) for n = 2, 3. This is done without charts or any local linearization, and instead is performed by operating over the orbitope of rotation matrices. This results in a novel model predictive control (MPC) scheme without the drawbacks associated with conventional linearization techniques such as slow computation time and local minima. Of particular emphasis is the application to aeronautical and vehicular systems, wherein the method removes many of the trigonometric terms associated with these systems’ state space equations. Furthermore, the method is shown to be compatible with many existing variants of MPC, including obstacle avoidance via Mixed Integer Linear Programming (MILP).

Item Type:Thesis (Senior thesis (Major))
Subject Keywords:Motion planning, model predictive control, aerospace systems, UAV
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Awards:Library Friends Senior Thesis Prize Finalist, 2014
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Burdick, Joel Wakeman
Group:Senior Undergraduate Thesis Prize
Thesis Committee:
  • None, None
Defense Date:6 June 2014
Non-Caltech Author Email:tahuang08 (AT)
Record Number:CaltechTHESIS:06052014-200112345
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8490
Deposited By: Tiffany Huang
Deposited On:06 Jun 2014 19:24
Last Modified:25 Oct 2023 21:12

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