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Öğe Application of adaptive neural network based interval type-2 fuzzy logic control on a nonlinear system(2011) Önen Ü.; Kalyoncu M.; Tinkir M.; Botsali F.M.In this study, four adaptive neural network based fuzzy logic controllers (ANNFL) are designed and used as two controllers in terms of interval type-2 fuzzy logic control. The new controllers are called as adaptive neural network based interval type-2 fuzzy logic controller (ANNIT2FL) and applied to a rigid-flexible robot manipulator. Initially dynamic model of the manipulator is obtained by using Lagrange equations and assumed modes method. ANNFL controllers are used for tracking and vibration control of system. The training and testing data of ANNFLs are obtained from the conventional PD control of the manipulator system. The performances of four ANFLCs are tested for different type and different number of membership functions and combined to create two ANNIT2FL controllers. Finally simulation results are obtained according to rotation and vibration control performances of ANNIT2FL controllers. Results demonstrate the remarkable performance of the proposed control technique. © 2011 IEEE.Öğe Competition of fuzzy logic controllers applied on flexible manipulator(2011) Tinkir M.; Kalyoncu M.; Önen Ü.; Botsali F.M.This paper presents the performances of different type fuzzy logic controllers which are adaptive neural-network based fuzzy logic (ANNFL) controller, hierarchical adaptive neural-network based fuzzy logic (HANNFL) controller and adaptive neural-network based interval type2 fuzzy logic (ANNIT2FL) controller. ANNFL, HANNFL and ANNIT2FL controllers are applied on flexible manipulator for both position and tip deflection control to learn which one satisfy us with it's performance according to desired goals. The performances of the proposed controllers are evaluated on the basis of the experimental results. © 2011 IEEE.Öğe Modeling and control of scaled a tower crane system(2011) Tinkir M.; Önen Ü.; Kalyoncu M.; Sahin Y.In this paper, we proposed a hierarchical artificial neural network based adaptive fuzzy logic (HANNFL) control of flexible link carrying pendulum system which was assumed as scaled a tower crane system and capable to move in the horizontal plane. Simulation and experimental studies were realized to control position and tip displacement of the flexible link and swing angles of pendulum. The dynamic model of the system was obtained by using Lagrange formulation and three-dimensional solid modeling program. The results obtained from simulation and experimental works were compared and given by graphics and tables. The validity of simulations were verified by experimental studies and the performance of the desired controller was explored. The position, tip displacement and swing angle control of the system were carried out successfully both simulation and experimental studies and desired goals were achieved. © 2011 IEEE.Öğe Network based type-2 fuzzy logic controller design for hydraulic system(2011) Haydim M.; Tinkir M.; Kalyoncu M.; Önen Ü.In this study, an adaptive network based type-2 fuzzy logic (ANT2FL) controller is designed for position control of an electro hydraulic servo system. MATLAB/SIMULINK toolbox is used for mathematical modelling of the system and controller design. As a first step, mathematical models of the servo valve, hydraulic cylinder, and hydraulic power unit which are main parts of the system are obtained. In this model, compression capability of the hydraulic fluid, friction and inertia are taken into consideration. The sensitivity, dependability and stability of the controller is taking in to account for a reference trajectory. ANT2FL control structure is preferred because of complexity of the dynamic system behaviours and variability of the electro hydraulic servo system characteristics with time and it is built by using the feedback of the position error and derivative of the position error. Finally, simulation results are evaluated for different type square and sinus reference input signals. Results demonstrate the remarkable performance of ANT2FL. © 2011 IEEE.Öğe A new approach for interval type-2 by using adaptive network based fuzzy inference system(2011) Tinkir M.In this paper, adaptive network based fuzzy inference system (ANFIS) was used in control applications of different type nonlinear systems as interval type-2 fuzzy logic controller (IT-2FL). Two adaptive network based fuzzy inference systems were chosen to design type-2 fuzzy logic controllers for each control applications. Membership functions in interval type-2 fuzzy logic controllers were set as an area called footprint of uncertainty (FOU), which is limited by two membership functions of adaptive network based fuzzy inference systems; they were upper membership function (UMF) and lower membership function (LMF). The double inverted pendulum, a single flexible link and a flexible link carrying pendulum systems were used to test the performances of designed interval type-2 fuzzy logic controllers. System behaviours were defined by Lagrange formulation and MATLAB/SimMechanics computer simulations. The performances of the proposed controllers were evaluated and discussed on the basis of the simulation results. An experiment set up of flexible link carrying pendulum system was built and used to verify the performance of IT-2FL controller. © 2011 Academic Journals.Öğe PID control of inverted pendulum using adams and matlab co-simulation(Association for Computing Machinery, 2016) Çakan A.; Botsali F.M.; Tinkir M.This research is aimed at developing a multi-body simulation model and control of an inverted pendulum. A virtual prototype of the inverted pendulum is built by using MSC Adams software and the plant model is exported to MATLAB. It is co-simulated in both MATLAB and MSC Adams softwares together. Proportional-integral-derivative (PID) controller is designed and implemented in order to use in pendulum angle control simulations. The modelling and control results shows that the Proportional-integral-derivative (PID) controller can successfully achieve pendulum angle control of the inverted pendulum. Controlled pendulum angle results are simulated and given in the form of the graphics. © 2016 ACM.Öğe Prediction of a diesel engine characteristics by using different modelling techniques(2011) Berber A.; Tinkir M.; Gültekin S.S.; Çelikten I.In this study, the characteristics of a four-stroke internal combustion diesel engine have been investigated by means of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) modelling techniques, using injection pressure, engine speed and torque. Injection pressure of diesel engine has been designed with a pressure of 150 bars for the turbo charger and pre-combustion chamber. The experiments have been implemented for four different pressure values, namely 100, 150, 200 and 250 bars with throttle positions of 50, 75 and 95%. Brake means effective pressure (BMEP), fuel flow (FF), specific fuel consumption (SFC) were obtained from experimental results for four different injection pressure. The proposed ANNs and ANFIS models are composed of the results of implemented measurements. ANNs model of the diesel engine has two subsystem. The first subsystem has two outputs (BMEP, FF) and the second subsystem has single output as specific fuel consumption (SFC). In first subsystem ANNs model, both mean effective pressure and fuel flow parameters are computed concurrently. ANFIS model of system has three inputs and outputs as injection pressure, engine speed, torque, BMEP, FF and SFC, respectively. The performance of ANNs and ANFIS models are compared with each other in same figures for same experimental data. The results of modeling techniques of a four-stroke internal combustion diesel engine are observed to be very close with the experimental results. ©2011 Academic Journals.Öğe Trajectory planning and adaptive neural network based interval type-2 fuzzy logic controller design of 3-DOF robot(2011) Şahin Y.; Tinkir M.; Ankarali A.In this paper, design of an adaptive neural network based interval type-2 fuzzy logic controller (ANNIT2FL), circular and handwriting type trajectory planning are proposed to show ability of a 3-DOF( Degree of Freedom ) Scara type robot manipulator. The kinematic and the dynamic equations are used to obtain equations of motion of robot manipulator and three different rise functions are chosen for desired cartesian trajectory as circular tool trajectory. Then, handwriting type trajectory is created to test controller's performance. Trajectory results of ANNIT2FL controlled robot manipulator are compared with the results of PID control. Simulation results demonstrate the remarkable performance of the proposed control technique. © 2011 IEEE.
