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Convex Model Predictive Control for Vehicular Systems

Huang, Tiffany A. and Horowitz, Matanya B. and Burdick, Joel W. (2014) Convex Model Predictive Control for Vehicular Systems. . (Unpublished)

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In this work, we present a method to perform Model Predictive Control (MPC) over systems whose state is an element of 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 MPC scheme without the drawbacks associated with conventional linearization techniques. Instead, second order cone- or semidefinite-constraints on state variables are the only requirement beyond those of a QP-scheme typical for MPC of linear systems. Of particular emphasis is the application to aeronautical and vehicular systems, wherein the method removes many of the transcendental 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:Report or Paper (Discussion Paper)
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Record Number:CaltechAUTHORS:20190410-120644197
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94636
Deposited By: George Porter
Deposited On:10 Apr 2019 19:53
Last Modified:03 Oct 2019 21:05

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