Presentation Date: Feb 14, 2026
AGSA Abstract
Industrial conveyor lines play a critical role in modern manufacturing, especially in Nigeria and Africa, yet they are often operated using fixed-speed drives and rule-based logic that do not adapt to varying load and process conditions. This paper presents a comprehensive Model-Based Predictive Control (MPC) architecture designed to optimize conveyor speed, buffer capacity, and station coordination in real time. A physics-based dynamic model of the conveyor, including motor drive characteristics, frictional losses, material load dynamics, and inter-station timing, was developed to predict system states. The MPC algorithm solves a rolling-horizon optimization problem that minimizes congestion, energy consumption, and idle time while satisfying operational constraints and actuator limits. The framework was implemented in MATLAB/Simulink and deployed in a virtual PLC environment using TwinCAT XAE. Simulation results indicate a considerable percentage increase in throughput, smoother load distribution, and a measurable reduction in motor energy consumption. The proposed approach demonstrates a scalable, Industry 4.0–aligned strategy for intelligent, adaptive, and resource-efficient material handling systems.
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