1,721,005 research outputs found
Visual Servoing Application for Inverse Kinematics of Robotic Arm Using Artificial Neural Networks
No full textThis paper presents novel approach for a visual servoing application of six axis robotic arm. Basic image-processing techniques were used for object recognition and position determination of robotic arm. The inverse kinematics solution of the robot arm was performed with artificial neural networks. Afterwards the robot's inverse kinematics solution was completed, the determined joint-angle values were used to control the robot arm. Performance of radial basis function network (RBF) and multilayer perceptron (MLP) were also compared
Field Programmable Gate Arrays Based Real Time Robot Arm Inverse Kinematic Calculations and Visual Servoing
No full textReliability and precision are very important in space, medical, and industrial robot control applications. Recently, researchers have tried to increase the reliability and precision of the robot control implementations. High precision calculation of inverse kinematic color based object recognition, and parallel robot control based on field programmable gate arrays (FPGA) are combined in the proposed system. The precision of the inverse kinematic solution is improved using the coordinate rotation digital computer (CORDIC) algorithm based on double precision floating point number format. Red, green, and blue (RGB) color space is converted to hue saturation value (HSV) color space, which is more convenient for recognizing the object in different illuminations. Moreover, to realize a smooth operation of the robot arm, a parallel pulse width modulation (PWM) generator is designed. All applications are simulated, synthesized, and loaded in a single FPGA chip, so that the reliability requirement is met. The proposed method was tested with different objects, and the results prove that the proposed inverse kinematic calculations have high precision and the color based object recognition is quite successful in finding coordinates of the objects
Hyperspectral Image Classification Based on Multilayer Perceptron Trained with Eigenvalue Decay
Get PDFHyperspectral Images (HSI) require sufficient labeled samples and a complex classifier to identify an area. Support Vector Machine (SVM) is one of the most competent algorithms in this field. Neural Networks (NN) is another approach used for classification problems, and both have been widely proposed in the literature. The Convolutional Neural Network (CNN) method has also received significant attention in the deep learning field recently. Nevertheless, during NN training, the overfitting problem may cause continuous dragging of the algorithm toward larger error. In this case, a regularization technique is needed to constitute the most useful decision boundary. The Eigenvalue Decay method is one of the regularization techniques that may be applied for HSI. This study investigates the performance of Multilayer Perceptron trained with an Eigenvalue Decay (MLP-ED) algorithm for HSI classification. The SVM, CNN with Pixel-Pair and CNN-Ensemble methods are used as comparison algorithms for MLP-ED performance assessment. All methods were tested with 3 different high-resolution HSI datasets. While SVM is one of the classic classifiers, and the 2 new CNN algorithms show high performance, the proposed MLP-ED method has more computational efficiency and achieves higher success than the others do
Pneumatic motor speed control by trajectory tracking fuzzy logic controller
No full textIn this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is defined to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to find the TTFLC boundary values of membership functions (MF) and weights of control rules. In addition, artificial neural networks (ANN) modelled dynamic behaviour of PM is given. This ANN model is used to find the optimal TTFLC parameters by offline GA approach. The experimental results show that designed TTFLC successfully enables the PM speed track the given trajectory under various working conditions. The proposed approach is superior to PID controller. It also provides simple and easy design procedure for the PM speed control problem
Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator
No full textControlling of a robotic arm using a brain-computer interface (BCI) is one of the most impressive applications. In this study, a novel method for the classification of motor imaging (MI) electroencephalography (EEG) signals are proposed for BCI. EEG signals are divided into three secondary tables, which were converted into spectrogram images. After applying the spectrogram method, the obtained images are divided into folder structures and deep learning is performed. In the deep learning stage, 400 images are obtained for each task as input to the Goo-gLeNet. After the deep learning completed, the presented system has been tested to imagine up, down, left and right movement to control the movement of the robot arm. It is observed that the robot arm performs the desired movement over 90% accuracy
Soft computing technique for power control of Triga Mark-II reactor
No full textIn this study, a trajectory tracking fuzzy genetic controller for Istanbul Technical University Triga Mark-II nuclear research reactor design approach is given. Power output of reactor is controlled along the predefined trajectory by fuzzy logic controller. Designed zero order Sugeno type fuzzy logic controller membership boundary value and rule weights are found by genetic algorithm. Non-chattering control with smooth control surface is also achieved using constrained fitness functions. Simulation results shows that reactor power successfully tracks the given trajectory under various working conditions and reaches the desired power level within the determined period within small tracking error. (C) 2011 Elsevier Ltd. All rights reserved
Development of a Remote Laboratory Infrastructure and LMS for Mechatronics Distance Education
No full textTrajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process
Get PDFThe main goal of this study was to compare the performances of genetic algorithm (GA) and ant colony optimization (ACO) algorithm for PID controller tuning on a pressure control process. GA and ACO were used for tuning of the PID controller when predefined trajectory reference signal was applied. Offline learning approach was employed in both GA and ACO algorithms. Realized pressure process dynamic has nonlinear behavior, thus system was modeled by nonlinear auto regressive and exogenous input (NARX) type artificial neural network (ANN) approach. PID controller was also tuned by ZieglerNichols (ZN) method to compare the results. A cost function was design to minimize the error along the defined cubic trajectory for the GA-PID and ACO-PID controller. Then PID controller parameters (Kp, Ki, Kd) were found by GA-PID, ACO-PID algorithms, which were adjusted with their optimal parameters. It was concluded that both ACO and GA algorithms could be used to tune the PID controllers in the pressure process with excellent performance. This material is suitable for an engineering course on neural networks, genetic algorithm, ant colony optimization and process control laboratory. (c) 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 20: 518528, 201
Modeling of dimmable High Power LED illumination distribution using ANFIS on the isolated area
No full textHigh power light emitting diodes (HP-LEDs) are more suitable for energy saving applications and have becoming replacing traditional fluorescent and incandescent bulbs for its energy efficient. Therefore. HP-LED lighting has been regarded in the next-generation lighting. In this study, illumination distribution of white color HP-LED is modeled by adaptive neuro-fuzzy inference system (ANFIS) approach on the isolated area while LED head is fixed. Subtractive clustering with hybrid learning approach is used to train the realized ANFIS architectures. End of the numerous experiment we finally concluded that, ANFIS could be used to modeling the illumination distribution applications perfectly. (C) 2011 Elsevier Ltd. All rights reserved
Smart indoor LED lighting design powered by hybrid renewable energy systems
No full textIt's important to provide diversity in energy resources and to use sustainable energy resources besides local resources. In the recent years, LEDs have been increasingly used in interior lighting systems because of their low energy consumption, high light-flux efficiency and their ability to maintain the light-flux value at a constant level for a long period of time. In this study, a LED luminaire was designed by using the Osram Coinstar W4 brand power LED module. An LED drive card, which is a specialty of the luminaire, was designed to provide ideal functioning conditions and serve as a power supply for the LEDs. A communication card was designed for all kinds of information exchange between the LED luminaires, motion detectors and light sensors used with the laboratory lighting system, and an RS485 communication line was installed to connect every system. Under the control of the luminaire, the lighting was tested by a fuzzy-expert system. With this project design and test, a system was developed that can be used in interior spaces. (C) 2017 Elsevier B.V. All rights reserved
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