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ABSTRACT:The 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 ZieglerÀNichols (ZÀN) 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 (K p , K i , K d ) 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. ß

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The problem in schools with mechatronics programs is not only their low student capacity but also their theoretical-content-dominated curricula. However, the manufacturing industry needs highly-talented technical personnel with a lot of practice in this field. E-Learning Education for Mechatronics Remote Laboratory, EDUMEC, offers practical-content-dominated courses over the Internet. The situations and expectations of the target groups were designated using surveys to constitute the educational concepts for the e-mechatronics program and experiments. The target groups are students in vocational schools or engineering classes; engineers or technicians who have completed formal education in mechanical engineering, electrical/electronic engineering, or other technical fields; employers; and teachers. For training, a special multilingual e-learning platform has been built in a user-friendly environment with a Learning Management System (LMS). This paper presents the design and implementation of the remote laboratory and LMS. In addition, evaluations by the students who participated in the training are presented.

This paper presents novel approach for a visual servoing application of six axis robotic arm. Basic imageprocessing 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.

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