MODELS AND METHODS OF OPERATIONAL MANAGEMENT OF MOBILE PLATFORMS IN CONDITIONS OF DYNAMIC INSTABILITY AND INFORMATION UNCERTAINTY

Abstract

The paper is devoted to the problem of ensuring dynamic stability of operational control systems for mobile platforms under conditions of high informational uncertainty inherent to modern Smart City logistics environments. The study is concerned with the degradation of traditional telemetry sources caused by the absence of full IoT sensor coverage and the suppression of GNSS/GPS signals, which leads to the transformation of the system into a nonlinear dynamic regime with features of deterministic chaos. The main objective of the research is to develop a method for real-time operational control of mobile platforms based on the integration of chaos theory tools for assessing and predicting the stability of logistical plans. The paper considers the application of nonlinear dynamics, time-series analysis, phase space reconstruction using Takens’ theorem, and information theory approaches to evaluate system uncertainty. It is proposed to use the largest Lyapunov exponent as a quantitative indicator of system sensitivity to small perturbations. As it is shown in the paper, when λ > 0, the system enters a chaotic regime characterized by exponential divergence of trajecto- ries and loss of planning reliability. This allows early identification of bifurcation points, where minor deviations lead to critical instability of the entire service schedule. The paper describes a method for reconstructing the phase space of the system based on indirect telemetry obtained via RFID verification. This approach makes it possible to estimate hidden dynamics of waste accumulation processes even in the absence of direct sensor measurements. Special attention is paid to the improvement of a three-level control architecture by integrating Edge Computing and swarm intelligence principles. It is shown that such an approach enables decentralized self-organization of mobile platforms and realtime task redistribution under communication loss conditions. The results of simulation experiments demonstrate that the proposed method improves the robustness of routing plans, reduces system entropy, and enables proactive adaptation to dynamic disturbances. The practical significance of the work lies in the development of resilient Smart City systems capable of maintaining operational efficiency under conditions of incomplete data and external disruptions