Yazar "Arpaci, Huseyin" seçeneğine göre listele

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    ANFIS & PI?D? controller design and comparison for overhead cranes
    (Natl Inst Science Communication-Niscair, 2011) Arpaci, Huseyin; Ozguven, O. Faruk
    The study suggests a method of designing an intelligent digital control for maintaining the load masses and target displacement at predefined position on overhead crane system. The control works on the basis of modeling at position control and sway angle control. In this study, the ANFIS and (PID mu)-D-lambda control system are applied to crane. Heuristic rules derived with the membership functions then the parameters of membership functions are tuned by adaptive neuro-fuzzy inference system (ANFIS). MATLAB, SIMULINK and Fuzzy Logic TOOLBOX are the programming environments used for realization of the model. The principle aim in designing the control is to assure the fastest and best transition possible from a controller for overhead crane. A non-linear model for an overhead crane system, which takes into account a combination of a trolley and a pendulum, is derived. The overall mathematical model obtained is simulated using MATLAB-SIMULINK. An adaptive neuro-fuzzy controller, which includes three rule bases, and PID, (PID mu)-D-lambda, used for position control, is successfully designed and implemented on the below simulated model. At the same time, in this performance, more rapid and less swing results are obtained for longer transportation distance with ANFIS control system, compared with the results of other studies.
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    Design of Adaptive Fractional-Order PID Controller to Enhance Robustness by Means of Adaptive Network Fuzzy Inference System
    (Springer Heidelberg, 2017) Arpaci, Huseyin; Ozguven, Omerul Faruk
    In this paper, a tuning strategy for the design of fractional-order proportional-integral-derivative ((PID mu)-D-lambda) controllers is proposed. First, a (PID mu)-D-lambda controller is designed with genetic algorithm in order to obtain the training data. Then, three Adaptive Network Fuzzy Inference System (ANFIS) structures, related to K-p, K-i and K-d parameters of the (PID mu)-D-lambda controller, are formed by using the training data. These ANFIS structures are used in the (PID mu)-D-lambda controller instead of K-p, K-i and K-d parameters, and they are capable of self-tuning during the simulation based on the input signal of the adaptive (PID mu)-D-lambda controller (ANFIS-(PID mu)-D-lambda). Finally, in order to show the control performance and robustness of the proposed parameters adjustment method with ANFIS, simulation results are obtained by using the MATLAB-Simulink program for two different systems and the results obtained from ANFIS-(PID mu)-D-lambda controller are compared with the results of (PID mu)-D-lambda and fuzzy logic controller.