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Author: search.filters.author.Limnararat, SunpasitSubject: search.filters.subject.Neural Network

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    Decentralized control with neural network of cooperative robot manipulator for object balancing task on flat plate
    (2017-12-13)
    Jaisumroum, Nattapon
    ;
    Chotiprayanakul, Pholchai
    ;
    Limnararat, Sunpasit
    In this paper, a decentralized framework for kinematic control of cooperative manipulators systems is developed. The motion of the robot system is specified at the object position, by adopting a task-oriented formulation for cooperative tasks. In the controller of robot computes, the end-effector motion of robots in a decentralized on the camera position frame of the knowledge of the assigned cooperative task. The motion of manipulator is reference computed by object and its neighbors. The joint motion of robots is reference corresponding from the end-effector. This study approach decentralized control of collaborative manipulators, tested in the simulation on MATLAB® software composed by neural network method. A neural network is used to approximate a decentralized control law designed by the back-propagation technique. The motion for each joint is controlled independently using local angular position and velocity measurements. Finally, the experiment shows the feasibility of the proposed control scheme using a robotics.
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    Self-tuning control with neural network for robot manipulator
    (2016-01-24)
    Jaisumroum, Nattapon
    ;
    Chotiprayanakul, Pholchai
    ;
    Limnararat, Sunpasit
    This paper presents an approach of the self-tuning control with neural network for robot manipulator in an object balancing task. A 3DOF robot arm (Novint Falcon 3D haptic) is used to hold a flat plate balancing a cylindrical object put on. Since a neural network algorithm were presented earlier in [1], [2] in order to learn and control the posture of the robot, we now employ the visual feedback into neural network to enable the robot arm learn to move its end effector. A webcam is used to determine position of a cylinder object rolling on a flat plate that the robot is holing. The images are processed to the object position by neural network. The output of the neural network is the height of the robot's end-effector that the robot has to lift the plate. The neural network must learn and self-calibrate by some repeating trail movements until the virtual feedback enable the robot arm adopts recalibrating parameters to stabilize the rolling task. The results of experiments show the learning procedure of the neural network is succeed in self-tuning control.