Adaptive control architecture for battlefield environments with a cybernetic prediction contour

The paper presents the concept and results of research into the architecture of adaptive combat environment control using a cybernetic prediction loop. Classic approaches to the formation of human-machine interface systems demonstrate limited stability in stressful conditions, while modern technologies of digital twins and artificial intelligence open up opportunities for new approaches to the creation of control architectures capable of adaptively balancing operator intervention and automation of robotic system control algorithms. The aim of the study is to develop a new generation of adaptive control architecture that reduces uncertainty, increases confidence in automated decisions, and protects against dangerous scenarios. To achieve this goal, digital twins of the operator and the robotic system, an AI assistant as an integration node, and a cybernetic prediction circuit for multi-scenario modeling of possible actions were used. The mathematical model includes entropy indicators as a measure of uncertainty, a connectivity matrix to reflect the consistency of twins, and a trust function τ, which determines the degree of control delegation from human to machine. A monotonicity theorem of trust has been proposed and proven, formalizing the tendency to increase the level of trust in the process of system training. The experimental results showed that the proposed architecture provides a 30–40% reduction in prediction entropy, a 30% increase in confidence compared to baseline models, a 25–40% reduction in operator involvement, and stable passage through safety filters even in complex extreme conditions. The scientific novelty of the research lies in the introduction of a cybernetic prediction loop mechanism into the adaptive control architecture, the formalization of the monotonicity theorem of confidence, and the use of ensemble simulations to evaluate future scenarios. The practical significance lies in the possibility of increasing the efficiency and safety of extreme robotic complexes, including combat ones, reducing the cognitive load on the operator, and creating the preconditions for the introduction of new standards of human-machine interaction in the miltech industry. At the same time, the work revealed a number of limitations, including increased computational costs, dependence on the accuracy of digital twin models, and the need to protect the architecture from cyber interference. The results obtained provide a scientific and technical basis for moving from the conceptual level to the experimental and testing stage and integrating the developed architecture into real next-generation combat systems.