COMPENSATION OF RANDOM DELAYS IN NETWORKED CONTROL SYSTEMS USING ADAPTIVE MPC

Abstract

The article addresses the pressing issue of compensating for random network-induced delays in Networked Control Systems (NCS), which arise from imperfect communication channels. These stochastic delays, caused by packet queues, collisions, and variable network load, lead to system destabilization, degraded accuracy, and reduced performance. This is especially critical in high-stakes applications such as industrial automation and telemedicine. Traditional compensation methods, including PID control and deterministic approaches (e.g., the Smith predictor), prove ineffective against the random nature of these delays. The proposed solution employs Model Predictive Control (MPC), which leverages a system model to predict future behavior and optimize control actions to compensate for anticipated signal distortions. The work aims to develop and verify a compensation method based on adaptive MPC. It presents a mathematical model of an NCS that accounts for random delays in the sensor-controller and controller-actuator channels, formalizing an extended state vector and constructing an observer. Practical significance is confirmed through a MATLAB/Simulink simulation model with reproducible stochastic delays and a quantitative analysis of control performance degradation. The study proposes and analyzes a hybrid solution combining a custom state prediction algorithm, an adaptive control buffer, and tools from the MPC Toolbox. Comparative simulations demonstrate the superiority of the hybrid approach over both standard MPC tools and custom predictors in key metrics: Integral Absolute Error (IAE), settling time, control energy, and robustness to increased delays. The results show a significant improvement in system stability and performance, confirming the promise of adaptive MPC for building reliable next-generation control systems capable of operating in non-ideal communication environments.