TITLE:
Adaptive Mechatronic Monitoring and Control of Thermal-Humidity Conditions in Stored Cotton Stacks Based on Recursive Least-Squares Identification
AUTHORS:
Isamidin Sidikov, Surojiddin Yuldashev
KEYWORDS:
Adaptive Control, Recursive Least Squares, Online Identification, Cotton Storage, Mechatronic System, Sensor Fusion, Supervisory Control
JOURNAL NAME:
Intelligent Control and Automation,
Vol.17 No.4,
October
23,
2026
ABSTRACT: Long-term storage of raw cotton in stacks is accompanied by spatially non-uniform thermal and humidity fields, and a locally hazardous state can develop unnoticed when the stack is observed at a single point or at long manual inspection intervals. This paper proposes a closed-loop mechatronic architecture that unites eight distributed temperature-humidity sensing nodes with wireless data transmission, validity-aware sensor fusion, a multi-factor technological risk index, recursive least-squares (RLS) identification of the effective cooling coefficient with exponential forgetting (used for model adaptation and diagnosis of ventilation effectiveness), a six-state supervisory automaton and a mobile stepper-driven ventilation actuator protected by independent hardware safety circuits. In contrast to monitoring-only solutions, measurement validation, online identification, risk-based decision-making, positioning and active ventilation are coordinated within a single control loop, and the information uncertainty caused by lost sensor nodes is treated separately from the technological risk. The architecture was implemented in MATLAB/Simulink with eight first-order thermal channels, sensor faults and communication delays, fan/induction-motor and stepper-drive subsystems, and was examined in five scenarios: nominal operation, multiple hot zones, sensor disconnection, a 30% loss of fan effectiveness and a 40% increase in movement resistance. Against the virtual validation series the identified model gave RMSE = 0.1197˚C, MAE = 0.0965˚C, MAPE = 0.343% and R2 = 0.9689. These values characterise agreement with simulated data only and are not claimed as physical validation on a real cotton stack. The study thus provides a structured path from distributed monitoring to adaptive mechatronic control, together with a staged experimental protocol and quantitative acceptance criteria for subsequent validation.