A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems. The identification of the system to be controlled is realized by a three-layer feed-forward neural network model. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. The neural network model is then used as the system simulator for developing the predictive fuzzy logic controller. The use of fuzzy clustering facilitates automatic generation of membership relations of the input-output data. Unlike the linguistic fuzzy logic controller which requires approximate knowledge of the shape and the numbers of the membership functions in the input and output universes of the discourse, this integrated neuro-fuzzy approach allows one to find the fuzzy relations and the membership functions more accurately. Furthermore, there is no need for tuning the controller. A nonlinear heating/cooling system is chosen as an application. The performance of this predictive fuzzy controller is shown to be superior to that of on/off, PI and linguistic fuzzy logic controllers in terms of both the accuracy and the consumption of energy.
|Number of pages||12|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - Jun 13 1995|
|Event||Applications of Fuzzy Logic Technology II 1995 - Orlando, United States|
Duration: Apr 17 1995 → Apr 21 1995