Yazar "Kalyoncu, M." seçeneğine göre listele

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    Automatic design of control systems for robot manipulators using the bees algorithm
    (SAGE PUBLICATIONS LTD, 2012) Fahmy, A. A.; Kalyoncu, M.; Castellani, M.
    This paper proves the capability of the bees algorithm to solve complex parameter optimization problems for robot manipulator control. Two applications are presented. The first case considers the modelling of the inverse kinematics of an articulated robot arm using neural networks. The weights of the connections between the nodes need to be set so as to minimize the difference between the neural network model and the desired behaviour. In the proposed example, the bees algorithm is used to train three multilayer perceptrons to learn the inverse kinematics of the joints of a three-link manipulator. The second case considers the design of a hierarchical proportional-integral-derivative (PID) controller for a flexible single-link robot manipulator. The six gains of the PID controller need to be optimized so as to minimize positional inaccuracies and vibrations. Experimental tests demonstrated the validity of the proposed approach. In the first case, the bees algorithm proved very effective at optimizing the neural network models. Compared with the results obtained employing the standard back-propagation rule and an evolutionary algorithm, the bees algorithm obtained superior results in terms of training accuracy and robustness. In the second case, the proposed method demonstrated remarkable efficiency and consistency in the tuning of the PID controller parameters. In 50 independent optimization trials, the PID controllers designed using the bees algorithm consistently outperformed a robot controller designed using a standard manual technique.
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    Etiology and clinical features of acute renal failure in Turkey: A nationwide prospective study
    (SPRINGER, 2008) Duzova, A.; Bakkaloglu, A.; Kalyoncu, M.; Poyrazoglu, H.; Delibas, A.; Ozkaya, O.; Peru, H.
    [Abstract not Available]
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    Modelling of Neurofuzzy Control of a Flexible Link
    (SAGE PUBLICATIONS LTD, 2010) Tınkır, M.; Önen, U.; Kalyoncu, M.
    A modelling approach for neuro-fuzzy control of a single-link flexible robot manipulator that uses a computer-aided design (CAD) program is proposed. Initially, a CAD model of the flexible link is created using experimentally determined values of system parameters. This CAD model is then exported to MATLAB software and the Simulink/SimMechanics toolbox. An adaptive-network-based fuzzy logic controller is used for position and vibration control of the flexible link. Experimental and simulation results are presented that validate the proposed approach.
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    Optimal tuning of PID controller using grey wolf optimizer algorithm for quadruped robot
    (2018) Şen, M. A.; Kalyoncu, M.
    The research and development of quadruped robotsis grown steadily in during the last two decades. Quadruped robotspresent major advantages when compared with tracked andwheeled robots, because they allow locomotion in terrainsinaccessible. However, the design controller is a major problem inquadruped robots because of they have complex structure. Thispaper presents the optimization of two PID controllers for aquadruped robot to ensure single footstep control in a desiredtrajectory using a bio-inspired meta-heuristic soft computingmethod which is name the Grey Wolf Optimizer (GWO)algorithm. The main objective of this paper is the optimization ofKP, KI and KD gains with GWO algorithm in order to obtain moreeffective PID controllers for the quadruped robot leg. Theimportance to this work is that GWO is used first time as adiversity method for a quadruped robot to tune PID controller.Moreover, to investigate the performance of GWO, it is comparedwith widespread search algorithms. Firstly, the computer aideddesign (CAD) of the system are built using SolidWorks andexported to MATLAB/SimMechanics. After that, PID controllersare designed in MATLAB/Simulink and tuned gains using thenewly introduced GWO technique. Also, to show the efficacy ofGWO algorithm technique, the proposed technique has beencompared by Genetic Algorithm (GA) and Particle SwarmOptimization (PSO) algorithm. The system is simulated inMATLAB and the simulation results are presented in graphicalforms to investigate the controller’s performance.