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Öğe Adaptive fuzzy logic with self-tuned membership functions based repetitive learning control of robotic manipulators(Elsevier Ltd, 2021) Yilmaz B.M.; Tatlicioglu E.; Savran A.; Alci M.With increasing demand for using robotic manipulators in industrial applications, controllers specific for performing repeatable tasks are required. These controllers must also be robust to model uncertainties. To address this research issue, a repetitive learning control method fused with adaptive fuzzy logic techniques is designed. Specifically, modeling uncertainties are first modeled with a fuzzy logic network and an adaptive fuzzy logic strategy with online tuning is designed. The stability is investigated via Lyapunov type techniques where global uniform ultimate boundedness of closed loop system is guaranteed. Numerical simulation results obtained from a two degree of freedom robot manipulator model and experiments performed on a robot manipulator demonstrate the efficacy of the proposed control methodology. © 2021 Elsevier B.V.Öğe Application of a neural network for estimating the crack formation and propagation in sol-gel CeO2 coatings during processing at temperature(Institute of Metals Technology, 2014) Savran A.; Alci M.; Yildirim S.; Yig^it R.; C¸elik E.In this study the application of a neural network to estimate the crack propagation and crack size in CeO2 coatings on a Ni substrate during processing at temperature was evaluated as a function of the Ce content in solutions with increasing processing temperatures from 24 oC and 700 oC. In this respect, CeO2 coatings were prepared on Ni tapes from solutions derived from Ce-based precursors using a sol-gel method for YBCO-coated conductors. The crack size of the coating was determined using an in-situ Hot-Stage ESEM depending on the temperature at a certain time in vacuum conditions. It was determined that the crack size of the coating increased with the increasing processing temperature. Measuring the crack sizes of the coatings using Hot-Stage ESEM is an expensive and time-consuming process. In order to eliminate these kinds of problems a neural-network approach was used to estimate the crack sizes of the coatings at different temperatures. The neural network was constructed directly from the experimental results. It was concluded that the estimation of the crack propagation of CeO2 coatings on a Ni tape substrate are reasonable for the processing temperatures.Öğe Dynamic analysis and control of a Quadrocopter(2013) Selim E.; Uyar E.; Alci M.Quadrocopter is an aerial vehicle platform which has become very popular among researchers in the recent past due to the advantages it offers over conventional helicopters. Quadrocopter is very simple structure, but it is inherently unstable from aerodynamics point of view. The first step before the control stage is presenting the adequate modeling of the system dynamic. This paper presents the dynamic model of quadrocopter as regarding to Newton's Laws with kinematic equations. Presented model is simulated with Matlab-Simulink for testing translational and rotational situations. And it is figured out that; presented model is acting like a real quadrocopter. Finally a PID controller designed for stabilize the altitude of modeled quadrocopter. © (2013) Trans Tech Publications, Switzerland.Öğe Fuzzy rule-base driven orthogonal approximation(2008) Alci M.In this study, orthogonal approximation concept is applied to fuzzy systems. We propose a new useful model adapted from the well-known Sugeno type fuzzy system. The proposed fuzzy model is a generalization of the zero-order Sugeno fuzzy system model. Instead of linear functions in standard Sugeno model, we use nonlinear functions in the consequent part. The nonlinear functions are selected from a trigonometric orthogonal basis. Orthogonal function parameters are trained along with the Sugeno fuzzy system. The proposed model is demonstrated using three simulations-a nonlinear piecewise-continuous scalar function modeling and filtering, nonlinear dynamic system identification, and time series prediction. Finally some performance comparisons are carried out. © 2007 Springer-Verlag London Limited.Öğe A fuzzy-neural approach for the characterisation of the active microwave devices(Institute of Electrical and Electronics Engineers Inc., 2002) Karlik B.; Torpi H.; Alci M.Artificial neural networks are emerging as a powerful technology for RF and microwave characterization, modeling, and design. A neural modeler helps us to immediately start developing neural models for RF/microwave components and circuits and helps to provide neural models for our simulators. In this study, a novel fuzzy neural network structure is used for behavior of an active microwave device. Here, the device is modeled by a black box whose small signal and noise parameters are evaluated through a fuzzy clustering neural network based upon the fitting of both of these parameters. © 2002 Weber Publ. Co.Öğe Generating Z-number by Logistic Regression(Institute of Electrical and Electronics Engineers Inc., 2021) Bilgin F.; Alci M.L. Zadeh came up with the idea of Z-number to reflect human decision-making ability in environments where information is uncertain. According to his idea, a Z-number consists of a classical fuzzy part and its reliability. Although there are linguistic based studies exist in the literature, designing the reliability part is still an open issue. In this paper, Logistic Regression is used to determine reliability part. Since the reliability part contains probability information and fuzzy granular information, both statistical and probability based methods must be proposed. The features such as giving probabilistic output, being optimization based via a cost function makes the Logistic Regression one of the best methods for generating Z-number. According to the proposed method, Z-numbers and Z-number based fuzzy if-then rules are written. We tried the Z-number based classifier on Fisher Iris Dataset. The results showed us the more reliable fuzzy membership functions give the more accurate outputs as expected. Another important issue is the reliability of input information, i.e. sensor data, was not known, so the reliability based calculations could not be performed. The reliability of input data can be calculated via proposed method. © 2021 IEEE.Öğe Mixed structured RBF network for direct inverse control of nonlinear systems(2009) Beyhan S.; Alci M.In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network has one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method with stable learning rate whose stable convergence behavior is proved by Lyapunov stability approach. The aim of construction of the proposed RBF network is to combine power of the networks which have different mapping abilities. These networks are autoregressive exogenous input model, nonlinear static NN model and nonlinear dynamic NN model. In simulations, the proposed network is applied for the direct inverse control of one benchmark nonlinear functioned system and Van de Vusse reaction in a CSTR discrete system even there exist large disturbances. From simulations, it is seen that the RBF network with stable leaming rate identifies and controls nonlinear systems accurately. ©2009 IEEE.Öğe A new RBF network based sliding-mode control of nonlinear systems(2009) Beyhan S.; Alci M.In this paper, a novel radial basis function (RBF) neural network is proposed and applied successively for online stable identification and control of nonlinear discrete-time systems. The proposed RBF network is a one hidden layer neural network (NN) with its all parameters being adaptable. The RBF network parameters are optimized by gradient descent method with stable learning rate whose stable convergence behavior is proved by Lyapunov stability approach. The parameter update is succeeded by a new strategy adapted from Levenberg-Marquardth (LM) method. The aim of construction of the proposed RBF network is to combine power of the networks which have different mapping abilities. These networks are auto-regressive exogenous input model, nonlinear static NN model and nonlinear dynamic NN model. To apply the model to control of the nonlinear systems, a known sliding mode control is applied to generate input of the system. From simulations; it is sown that the proposed network is an alternative model for identification and control of nonlinear systems with accurate results. © 2009 IEEE.Öğe A novel technique for classification of myoelectric signals for prosthesis(IFAC Secretariat, 2002) Karlik B.; Tokhi M.O.; Alci M.This paper presents an investigation into classifying myoelectric signals using a new fuzzy clustering neural network architecture for control of multifunction prostheses. Moreover, a comparative study of the classification accuracy of myoelectric signals using multi-layer perceptron with back-propagation algorithm, and the new fuzzy clustering neural network (FCNN) is presented. The myoelectric signals considered are used to classify four upper-limb movements, which are elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalise better than the multi-layer perceptron without requiring extra computational effort. The proposed neural network algorithm allows the user to learn better and faster. Copyright © 2002 IFAC.Öğe An orthogonal ARX network for identification and control of nonlinear systems(2009) Beyhan S.; Alci M.This paper presents a new orthogonal neural network (ONN) which is utilized successively for online identification and control of nonlinear discrete-time systems. The proposed network is designed with auto regressive with exogenous (ARX) terms of inputs and outputs, and their orthogonal terms by Chebyshev polynomials. The network is a single layer neural network and computationally efficient with less number of parameters. The identification by the network is performed in stable sense by using Lyapunov stability guaranteed learning rate. Hence, the learning rate depends on the current knowledge of the system instead of using constant learning rate. This learning rate provides fine online optimization. In simulation study, one benchmark nonlinear system is identified and results are compared. Then, one nonlinear functioned system is identified and controlled by model reference control. From results, it is seen that the proposed model has good learning capability for identification and control. ©2009 IEEE.Öğe Self-Adjusting Fuzzy Logic Based Control of Robot Manipulators In Task Space(Institute of Electrical and Electronics Engineers Inc., 2021) Yilmaz B.M.; Tatlicioglu E.; Savran A.; Alci M.End effector tracking control of robot manipulators subject to dynamical uncertainties is the main objective of this work. Direct task space control that aims minimizing the end effector tracking error directly is preferred. In the open loop error system, the vector that depends on uncertain dynamical terms is modeled via a fuzzy logic network and a self-adjusting adaptive fuzzy logic component is designed as part of the nonlinear proportional derivative based control input torque. The stability of the closed loop system is investigated via Lyapunov based arguments and practical tracking is proven. The viability of the proposed control strategy is shown with experimental results. Extensions to uncertain Jacobian case and kinematically redundant robots are also presented. IEEE
