This paper describes the model-based design and the experimental validation of a control system which suppresses the bouncing behavior of Compressed Natural Gas (CNG) fuel injectors. First a detailed model of the system is developed, including temperature and supply-voltage variation effects. Using an optical position sensor, this model is experimentally validated in a second step. Based on this model a feed-forward controller is developed and tested which minimizes the bouncing energy of the system. Since in series applications position sensing would be too expensive to use, an observer-based iterative control algorithm is derived which uses coil current measurements instead of the position information to asymptotically suppress bouncing.
Skip Nav Destination
Article navigation
March 2004
Technical Papers
Optimal Control for Bouncing Suppression of CNG Injectors
D. Dyntar,
D. Dyntar
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
Search for other works by this author on:
L. Guzzella
L. Guzzella
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
Search for other works by this author on:
D. Dyntar
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
L. Guzzella
Swiss Federal Institute of Technology (ETH), Zurich, Switzerland
Contributed by the Dynamic Systems, Measurement, and Control Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received by the ASME Dynamic Systems and Control Division October 28, 2002; final revision, June 24, 2003. Associate Editor: A. Alleyne.
J. Dyn. Sys., Meas., Control. Mar 2004, 126(1): 47-53 (7 pages)
Published Online: April 12, 2004
Article history
Received:
October 28, 2002
Revised:
June 24, 2003
Online:
April 12, 2004
Citation
Dyntar , D., and Guzzella, L. (April 12, 2004). "Optimal Control for Bouncing Suppression of CNG Injectors ." ASME. J. Dyn. Sys., Meas., Control. March 2004; 126(1): 47–53. https://doi.org/10.1115/1.1648311
Download citation file:
Get Email Alerts
Cited By
Offline and Online Exergy-Based Strategies for Hybrid Electric Vehicles
J. Dyn. Sys., Meas., Control (May 2025)
Multi Combustor Turbine Engine Acceleration Process Control Law Design
J. Dyn. Sys., Meas., Control
A Distributed Layered Planning and Control Algorithm for Teams of Quadrupedal Robots: An Obstacle-Aware Nonlinear Model Predictive Control Approach
J. Dyn. Sys., Meas., Control (May 2025)
Active Data-Enabled Robot Learning of Elastic Workpiece Interactions
J. Dyn. Sys., Meas., Control (May 2025)
Related Articles
Adjoint-Based Optimization Procedure for Active Vibration Control of Nonlinear Mechanical Systems
J. Dyn. Sys., Meas., Control (August,2017)
Model-Based Actuator Trajectories Optimization for a Diesel Engine Using a Direct Method
J. Eng. Gas Turbines Power (March,2011)
Preview Control for Vehicle Lateral Guidance in Highway Automation
J. Dyn. Sys., Meas., Control (December,1993)
An Optimal Control Approach to Minimizing Entropy Generation in an Adiabatic Internal Combustion Engine
J. Dyn. Sys., Meas., Control (July,2008)
Related Proceedings Papers
Related Chapters
QP Based Encoder Feedback Control
Robot Manipulator Redundancy Resolution
Isolated Handwritten Latin and Devanagari Numeral Recognition Using Fourier Descriptors and Correlation
International Conference on Mechanical and Electrical Technology, 3rd, (ICMET-China 2011), Volumes 1–3
Computation of Gradient and Hessian in Feed-Forward Neural Networks: A Variational Approach
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17