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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.
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    Fuzzy PID Control by Grouping of Membership Functions of Fuzzy Antecedent Variables with Neutrosophic Set Approach and 3-D Position Tracking Control of a Robot Manipulator
    (Springer Singapore Pte Ltd, 2018) Can, Mehmet Serhat; Ozguven, Omerul Faruk
    This paper aims to design of the neutrosophic fuzzy-PID controller and it has been compared with the conventional fuzzy-PID controller for position tracking control in terms of robustness. In the neutrosophic fuzzy-PID controller, error (e) and change of error (ce) were assessed separately on two fuzzy inference systems (FISs). In this study, the designed method is different from the conventional fuzzy logic controller design, membership degrees of antecedent variables were determined by using the T(true), I(indeterminacy), and F(false) membership functions. These membership functions are grouped on the universe of discourse with the neutrosophic set approach. These methods were tested on three-dimensional (3-D) position-tracking control application of a spherical robot manipulator in the MATLAB Simulink. In all tests, reference trajectory was defined for movements of all axes of the robot manipulator. According to the results of the study, when the moment of inertia of the rotor is changed, less overshoot ratio and less oscillation are obtained in the neutrosophic fuzzy-PID controller. Thus, our suggested method is seen to be more robust than the fuzzy-PID controllers.
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    PID Tuning with Neutrosophic Similarity Measure
    (Springer, 2017) Can, Mehmet Serhat; Ozguven, Omerul Faruk
    In this paper, a method for adjusting the proportional-integral-derivative (PID) coefficients based on the neutrosophic similarity measure is proposed. First, rough PID coefficients were determined by the Ziegler-Nichols method, and the upper and lower limit values for the search range of the PID coefficients were determined. At each step of the search range, we applied a unit step function to the system and obtained the transient response characteristics. The obtained values were converted into a neutrosophic set (real set) by using defined membership functions. Then, the optimal PID coefficients were obtained using the similarity ratio between the real and ideal (target) neutrosophic sets. In calculating the similarity ratio, the Hamming, Euclidean, Set-theoretic, Jaccard, and Dice approaches were applied, and the results were compared. Finally, the proposed method was tested on two transfer functions, and it was demonstrated that the proposed method can be used to adjust PID coefficients.