Model Predictive Control of a Direct Fire Projectile Equipped With Canards

Abstract

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.