0
Research Papers

Model Predictive Control of a Direct Fire Projectile Equipped With Canards

[+] Author and Article Information
Douglas Ollerenshaw

Department of Mechanical Engineering, Oregon State University, Corvallis, OR 97331

Mark Costello

School of Aerospace Engineering, Georgia Institute Of Technology, Atlanta, GA 30327

J. Dyn. Sys., Meas., Control 130(6), 061010 (Oct 01, 2008) (11 pages) doi:10.1115/1.2957624 History: Received September 12, 2004; Revised March 13, 2008; Published October 01, 2008

Launch uncertainties in uncontrolled direct fire projectiles can lead to significant impact point dispersion, even at relatively short range. A model predictive control scheme for direct fire projectiles is investigated to reduce impact point dispersion. The control law depends on projectile linear theory to create an approximate linear model of the projectile and quickly predict states into the future. Control inputs are based on minimization of the error between predicted projectile states and a desired trajectory leading to the target. Through simulation, the control law is shown to work well in reducing projectile impact point dispersion. Parametric trade studies on an example projectile configuration are reported that detail the effect of prediction horizon length, gain settings, model update interval, and model step size.

FIGURES IN THIS ARTICLE
<>
Copyright © 2008 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Schematic of the position coordinates of a direct fire projectile

Grahic Jump Location
Figure 2

Schematic of the attitude coordinates of a direct fire projectile

Grahic Jump Location
Figure 3

Uncontrolled dispersion (CEP=113.5ft centered at mean impact point)

Grahic Jump Location
Figure 4

Controlled dispersion (CEP=0.02ft centered at mean impact point)

Grahic Jump Location
Figure 5

Typical altitude versus range

Grahic Jump Location
Figure 6

Typical cross range versus range

Grahic Jump Location
Figure 7

Error between linear and nonlinear trajectory solutions

Grahic Jump Location
Figure 8

Required control inputs for a typical trajectory

Grahic Jump Location
Figure 9

Controlled dispersion with sensor bias and noise applied (CEP=2.1ft centered at mean impact point)

Grahic Jump Location
Figure 10

Controlled dispersion results as the control gain, r, is varied

Grahic Jump Location
Figure 11

Controlled dispersion results as the linear model update interval is varied

Grahic Jump Location
Figure 12

Controlled dispersion results as the arc length step size is varied

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In