Modeling Large-Scale Adversarial Swarm Engagements using Optimal Control

Abstract

We study optimal control of large-scale autonomous systems under adversarial conditions in which agents may be probabilistically destroyed during an engagement. Because attrition changes the population that generates the spatial interaction dynamics, treating survival and motion independently can produce physically inconsistent solutions. We formulate a stochastic variable-population benchmark and examine three deterministic approximations that propagate agent survival probabilities from relative geometry. The models are applied to defense of a high-value unit against an attacking swarm and solved using direct optimal-control methods. Monte Carlo realizations of the stochastic benchmark show that deterministic models can reproduce engagement outcomes when attrition is coupled to spatial influence, whereas a decoupled formulation can exploit effectively destroyed defenders that continue to repel attackers, a failure mode we term ``ghost herding.'' Large-scale simulations and defender-number sweeps further show that this modeling choice can substantially alter predicted high-value-unit survival and inferred defensive resource requirements. The results provide a tractable framework for attrition-aware trajectory optimization and resource--survival analysis in adversarial autonomous systems.