A METHOD OF ROBUST VARIATIONAL OPTIMIZATION OF OPERATIONAL TRAJECTORIES OF AN INFORMATION SYSTEM ON A MOBILE PLATFORM UNDER DESTRUCTIVE IMPACTS
DOI:
https://doi.org/10.35546/kntu2078-4481.2025.4.3.23Keywords:
information system, mobile platform, survivability, trajectory, optimization, robustnessAbstract
The paper addresses the problem of ensuring the survivability of an information system on a mobile platform under destructive impacts and strict resource constraints. The system’s operation is described in terms of discrete-time state trajectories, for which a set of states satisfying survivability requirements (survivability-admissible states) and the corresponding set of survivability-admissible initial states are introduced. On this basis, a variational trajectory optimization problem is formulated with a multicriteria performance functional that accounts for the deficit of service quality, resource expenditures, deviations of survivability indicators, and proximity to the boundary of the admissible region. For a set of destructive impact scenarios, a robust “worst-case” extension of the functional is constructed using a penalty representation of survivability constraints, which is interpreted as a requirement of invariance of the set of survivability-admissible states with respect to the most critical perturbations. Variational optimality conditions are derived, and an iterative gradient-type method is proposed for correcting the sequence of control actions, oriented toward scenarios that realize the maximal value of the robust functional. A simulation example for a two-dimensional state model is presented, demonstrating the advantages of the robust variational policy over a baseline strategy in terms of resource savings, postponing the exit of trajectories beyond the admissible region, and reducing worst-case integral costs. The obtained results extend known approaches of survivability theory and robust optimal control to the class of information systems operating on mobile platforms under destructive impacts and can serve as a basis for further research on the synthesis of adaptive resource allocation policies.
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