Articles

Predictive Pontryagin Optimal Control of Nonlinear Fully Distributed DCS and CPS under Reliability and Uncertainty Constraints

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The article provides an overview of the optimal control prediction framework for non-linear CPS and DCS in automation. The core issue is the fact that the present architecture of modern automation systems does not behave like a closed loop control system anymore. The design of these systems combines sensors, actuators, edge controllers, communication channels, digital twin, software-as-a-service, human involvement, and states of cyber-security. Thus, control design must not only address accuracy but should be a multicriteria design problem that accounts for reliability, uncertainty, probability mass, delay in communication, and energy costs. Four levels are considered in the proposed framework. The first level refers to forecasting based on hybrid ARIMA-ML model for a short horizon. The second level is concerned with estimating of risk states using Markov model/HMM.  The contribution is a simulation-ready mathematical architecture in which each node solves a local Hamiltonian problem using predicted states, neighbour information and reliability constraints, while the global CPS behavior emerges through networked local decisions. The paper formulates the nonlinear dynamics, cost functional, Hamiltonian conditions, reliability constraints and evaluation protocol for smart factories, smart grids, intelligent buildings and smart campuses. The framework is positioned as a bridge between predictive maintenance and optimal distributed automation control.

A Stochastic Framework for Fully Distributed Control Systems and CPS: From Local State Transitions to Global Uncertainty Propagation

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The transition from hierarchical automation toward fully distributed Distributed Control Systems (DCS) and Cyber-Physical Systems (CPS) creates a new class of engineering problems in which local intelligence, networked coordination and physical dynamics must operate under uncertainty. In these systems, control is no longer concentrated in a single supervisory unit. Instead, sensors, controllers, actuators, edge devices and cyber agents cooperate through local decisions and partial information. This article develops a stochastic framework for examining fully distributed DCS/CPS by linking three levels of analysis: how local states shift through Markov transitions, how short‑term decisions accumulate over time through the Chapman–Kolmogorov relation, and how uncertainty spreads in continuous processes through the Fokker–Planck equation. All these aspects indicate that a distributed system is something much more than a mere configuration of communication; it is an adaptive, stochastic controller organism, where its global functioning arises from many small local decisions that modify probabilities and paths. The design should then consider how these local decisions affect one another over time, rather than simply interconnecting devices. The proposed framework is then analyzed from the perspectives of stability, resilience, communication delay, cyber-security, scalability, energy efficiency and digital-twin-based prediction. The result is a theoretical foundation suitable for smart factories, smart grids, intelligent buildings, autonomous transport systems and future smart urban infrastructures. The added interpretative value of the framework is that each equation is treated not only as a formal mathematical relation, but also as a design logic. Markov probabilities are interpreted as local operational tendencies, Chapman-Kolmogorov composition as the logic of accumulated distributed decisions, and Fokker-Planck dynamics as the evolution of confidence, risk and uncertainty in the whole cyber-physical network.