An adaptive energy-efficient control allocation on planar motion control of electric ground vehicles

Yan Chen, Junmin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

An adaptive energy-efficient control allocation (A-EECA) is developed for planar motion control of electric ground vehicles (EGVs) with four in-wheel motors. Different from distribution processes in previous EECA designs [19][28], which needed to instantaneously solve nonlinear (even non-convex) optimization problems at each sampling time, this adaptive EECA approach can make distributed control actuation gradually converge to energy-optimal operating points. The consequent advantages are reflected in both low computational cost and free selections of initial conditions. Based on experimental data and some reasonable assumptions on the efficiencies of in-wheel motors, the adaptive EECA dictates different torque distributions to all the EGV wheels based on their different efficiencies. Simulation results of different maneuvers indicate that much less energy and computational cost are consumed when the adaptive EECA scheme is applied for controlling the planar motion of an EGV.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8062-8067
Number of pages6
ISBN (Print)9781612848006
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Country/TerritoryUnited States
CityOrlando, FL
Period12/12/1112/15/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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