Development of A Multiparametric Model Predictive Control System for Temperature Regulation of a Window – Type Air Conditioner

This project focuses on the development and evaluation of a multiparametric Model Predictive Control (mp-MPC) framework designed for the thermal regulation of a single-zone space conditioned by a window-type air-conditioning (AC) unit. Conventional online Model Predictive Control algorithms require significant real-time computational resources, limiting their deployment on low-cost embedded hardware. To address this limitation, the mp-MPC approach shifts the optimization burden offline, synthesizing an explicit piecewise-affine control law that can be executed efficiently in real-time. A lumped-parameter thermal dynamics model is formulated based on the first law of thermodynamics, and a discrete-time state-space representation is established to regulate both supply-air and room temperatures. The control system incorporates ambient temperature as

a measured disturbance. The performance of the mp-MPC system is analyzed and benchmarked against conventional online MPC, a Ziegler-Nichols-tuned PID controller, and a standard ON-OFF thermostat under various ambient temperature profiles, including constant high temperature, normal daytime variations, and extreme heatwave conditions. Evaluation metrics such as Mean Square Error (MSE), Integral Square Error (ISE), and Integral Absolute Error (IAE) are utilized to quantify the tracking performance and disturbance rejection capabilities of the proposed control strategy.

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