Identification and fuzzy logic control of nonlinear dynamical systems
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A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems. The proposed controller is particularly useful when the dynamics of the nonlinear system to be controlled are difficult to yield exact solutions and the system specification can be obtained in terms of crisp input-output pairs. It inherits the advantages of both fuzzy logic and predictive control. The identification of the nonlinear mapping of the system to be controlled is realized by a three-layer feed-forward neural network model employing the input-output data obtained from the system. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. 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. The proposed controller is applied to a nonlinear heating/cooling system and a multilink robot manipulator and it is shown that its performance is superior to the performances of the currently employed conventional controllers both in terms of accuracy and energy consumption.