In the present paper we develop an application of the optimal predictive control to building heating, ventilating and air conditioning (HVAC) systems. Explicit inequality constraints on the input and on the output of the system are considered. A specific model-based recursive parameter identification and fault detection approach is described. Simplified physical modeling of the process establishes the model structure of an adaptive predictor to be used both for identification and control. The adaptive predictor is based on the estimation of the states and the parameters of the process are updated using a two stages filtered instrumental projection method, described in the paper, that insures the promptness of fault detection.Fault detection is based on specific processing of the prediction errors and parameters of the predictor. Process diagnosis is ensured by appropriate use of the qualitative knowledge about the process. In order to increase the robustness of the fault detection scheme. additional test signals are introduced in the process. The resulting algorithm is a unified supervisory scheme for control identification and fault detection that provides information of failures in sensors and amators via diagnosis of abrupt changes in process parameters and discrimination of unmeasured disturbances that act on the system. Experimental results are presented.
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