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Robustness and Adaptability Analysis of Future Military Modular Fleet Operation System

[+] Author Affiliations
Xingyu Li, Bogdan I. Epureanu

University of Michigan, Ann Arbor, MI

Paper No. DSCC2017-5223, pp. V002T05A003; 10 pages
  • ASME 2017 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME


Modular vehicles are vehicles with interchangeable substantial components also known as modules. Fleet modularity provides a system with extra operational flexibility through on-field actions, in terms of vehicle assembly, disassembly, and reconfiguration. The ease of assembly and disassembly of modular vehicles enables them to achieve real-time fleet reconfiguration in order to reach time-changing combat environments and constantly update their techniques. Previous research reveals that life cycle costs, especially acquisition costs, shrink significantly as a result of fleet modularization. In addition, military field demands and enemy attacks are highly unpredictable and uncertain. Hence, it is of interest to the US Army to investigate the robustness and adaptability of a modular fleet operation system against demand uncertainty. We model the fleet operation management in a stochastic state space model while considering time delays from operational actions, as well as use model predictive control (MPC) to attain real-time optimal operation actions based on the received demands and predicted system status.

Analyses on the robustness and adaptability of how a modular vehicle fleet reacts to the demand disturbance and noise have been very limited, although research on operation management and model prediction control have been ongoing for many years. In our current study, we model all the main processes in a fleets operation into an integrated system. These processes include module resupply, vehicle transportation, and on-base assembly, disassembly, reconfiguration (ADR) actions. We also consider the fact that delayed field demands trigger additional demands, which might cause system instability under improper operational strategies. We have designed a predictive control approach that includes an optimizer and a simulation process to monitor and control the fleet operation. Under the identical mission demands and fleet configuration settings, a modular vehicle fleet shows a faster reaction speed than a conventional fleet once demand disturbance and noise are injected. Although our study is inspired by a military application, it is not hard to notice that our system also represents a simplified supply chain structure. Thus, our methodology can also be generalized for civilian applications.

Copyright © 2017 by ASME



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