The dynamics of guided projectile systems are inherently stochastic in nature. While deterministic control algorithms such as impact point prediction (IPP) may prove effective in many scenarios, the probability of impacting obstacles and constrained areas within an impact zone cannot be accounted for without accurate uncertainty modeling. A stochastic model predictive guidance algorithm is developed, which incorporates nonlinear uncertainty propagation to predict the impact probability density in real-time. Once the impact distribution is characterized, the guidance system aim point is computed as the solution to an optimization problem. The result is a guidance law that can achieve minimum miss distance while avoiding impact area constraints. Furthermore, the acceptable risk of obstacle impact can be quantified and tuned online. Example trajectories and Monte Carlo simulations demonstrate the effectiveness of the proposed stochastic control formulation in comparison to deterministic guidance schemes.
Stochastic Model Predictive Control for Guided Projectiles Under Impact Area Constraints
Woodruff School of Mechanical Engineering,
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received November 19, 2013; final manuscript received June 25, 2014; published online October 21, 2014. Assoc. Editor: Dejan Milutinovic.
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Rogers, J. (October 21, 2014). "Stochastic Model Predictive Control for Guided Projectiles Under Impact Area Constraints." ASME. J. Dyn. Sys., Meas., Control. March 2015; 137(3): 034503. https://doi.org/10.1115/1.4028084
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