Abstract-Recent efforts to incorporate aspects of artificial intelligence into the design and operation of automatic control systems have focused attention on techniques such as fuzzy logic, artificial neural networks, and expert systems. The use of computers for direct digital control highlights the recent trend toward more effective and efficient heating, ventilating, and airconditioning (HVAC) control methodologies. Researchers in the HVAC field have stressed the importance of self learning in building control systems and have encouraged further studies in the integration of optimal control and other advanced techniques into the formulation of such systems. Artificial neural networks can also be used to emulate the plant dynamics, in order to estimate future plant outputs and obtain plant input/output sensitivity information for on-line neural control adaptation. This paper describes a functional link neural network approach to performing the HVAC thermal dynamic system identification. Methodologies to reduce inputs of the functional link network to reduce the complexity and speed up the training speed will be presented. Analysis and comparison between the functional link network approach and the conventional network approach for the HVAC thermal modeling will also be presented.

The compensation of friction nonlinearities for servo motor control has received much attention due to undesirable and disturbing effects that the friction often has on conventional control systems. Compensation methods have generally involved selecting a friction model and then using model parameters to cancel the effects of the nonlinearity. In this paper, a method using fuzzy logic for the compensation of nonlinear friction is developed for the control of a dc motor. The method is unique in that a single fuzzy rule is used to compensate directly for the nonlinearity of the physical system. As a result, the method introduces fewer adjustable parameters than a typical fuzzy logic approach while still incorporating many advantages of using fuzzy logic such as the incorporation of heuristic knowledge, ease of implementation and the lack of a need for an accurate mathematical model. The general approach, analysis and experimental results obtained for an actual dc motor system with nonlinear friction characteristics are presented and the effectiveness of the fuzzy friction compensation control technique is discussed. The smoothness of response, response times and disturbance rejection of a PI control system with and without the proposed fuzzy compensator are analyzed and discussed to illustrate the effectiveness of the proposed method.

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