Date of Award

12-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Civil Engineering

Committee Chair/Advisor

Dr. Mashrur Chowdhury

Committee Member

Dr. Sakib Mahmud Khan

Committee Member

Dr. John Wagner

Abstract

In this study, the author theoretically develops and numerically validates an asymmetric linear bilateral control model (LBCM) for an automated truck platoon, in which the motion information (i.e., position and speed) from the immediate leading truck and the immediate following truck are weighted differently. The novelty of the asymmetric LBCM is that using this model, all the follower trucks in a platoon can adjust their acceleration and deceleration to closely follow a constant desired time headway at all times to improve platoon operational efficiency while maintaining local and string stability. The author theoretically proves the local stability of the asymmetric LBCM using the condition for asymptotic stability of a linear time-invariant system and derives the condition for string stability using a space headway error attenuation approach. Then, the author evaluates the efficacy of the asymmetric LBCM by simulating a closely coupled cooperative adaptive cruise control (CACC) platoon of fully automated trucks in various non-linear acceleration and deceleration states. To evaluate the platoon operational efficiency of the asymmetric LBCM, the author compares the performance of the asymmetric LBCM to a baseline model, i.e., the symmetric LBCM, for three different time headway settings, i.e., 0.6 sec, 0.8 sec, and 1.1 sec. Analyses indicate that the asymmetric LBCM yields lower sum of squared time headway error and sum of squared speed error compared to the baseline model considered in this study. These findings demonstrate the potential of the asymmetric LBCM in improving platoon operational efficiency and stability of an automated truck platoon.

Author ORCID Identifier

https://orcid.org/ 0000-0001-7326-3694

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.