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Journal : Proceeding of the Electrical Engineering Computer Science and Informatics

Active Fault Tolerance Control For Sensor Fault Problem in Wind Turbine Using SMO with LMI Approach Nuralif Mardiyah; Novendra Setyawan; Bella Retno; Zulfatman Has
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.511 KB) | DOI: 10.11591/eecsi.v5.1673

Abstract

In this paper, we start to investigate the sensor fault problem in a Wind Turbine model with Fault Tolerant Control (FTC). FTC is used to allow the parameters of the controller to be reconfigured in accordance error information obtained online from sensors to improve the stability and overall performance of the system when an error occurs. The design is divided into two parts. The first part is designed Sliding Mode Observer (SMO) based Fault Detection Filter (FDF) to generate a residual signal to estimate fault. FDF is designed to maximize sensitivity fault. The second is a design output feedback control and Fault Compensation to guarantee the stability and performance system from disturbance by ignoring faults. Moreover, the function of fault compensation is to minimize effect fault of the system. The main contribution of this research is FTC proved to solve the sensor fault problem in a Wind Turbine model. The simulation showed the effectiveness of this method to estimate the fault and stabilized the system faster to a steady condition.
Object Detection of Omnidirectional Vision Using PSO-Neural Network for Soccer Robot Novendra Setyawan; Nuralif Mardiyah; Khusnul Hidayat; Nurhadi Nurhadi; Zulfatman Has
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.1 KB) | DOI: 10.11591/eecsi.v5.1696

Abstract

The vision system in soccer robot is needed to recognize the object around the robot environment. Omnidirectional vision system has been widely developed to find the object such as a ball, goalpost, and the white line in a field and recognized the distance and an angle between the object and robot. The most challenging in develop Omni-vision system is image distortion resulting from spherical mirror or lenses. This paper presents an efficient Omni-vision system using spherical lenses for real-time object detection. Aiming to overcome the image distortion and computation complexity, the distance calculation between object and robot from the spherical image is modeled using the neural network with optimized by particle swarm optimization. The experimental result shows the effectiveness of our development in the term of accuracy and processing time.