Résumé :
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A neurocontrol methodology is presented for optimal temperature control of a thermal plant with multiple chambers. The plant, a household refrigerator, uses a simple cost-effective temperature control approach that provides chamber-dependent temperature control with minimized energy consumption at only a single control point. Technological advances in control hardware, including airflow control devices such as automatic thermal dampen, can add control degrees of freedom to achieve optimal control of temperature and energy consumption at all control points. The neurocontrol methodology presented uses the generalized learning approach for mapping the plant's inverse dynamics to the desired control signals. Two unconventional control strategies are examined: variable temperature bandwidths, and uncoupled compressor and evaporator fan operation. A plant model, representing the behavior of a conventional, dual chamber, top mount style refrigerator, was used to generate results for both strategies with manual and automatic thermal damper configurations.
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