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All Journal Perfecting a Video Game with Game Metrics Sinergi Proceeding of the Electrical Engineering Computer Science and Informatics TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Abdi Masyarakat (JAM) Journal FORTEI-JEERI International Journal of Engineering Continuity
Suwoyo, Heru
Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Sinergi

 1 
AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT Suwoyo, Heru; Tian, Yingzhong; Ibnu Hajar, Muhammad Hafizd
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.002

Abstract

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.
ENHANCING THE PERFORMANCE OF THE WALL-FOLLOWING ROBOT BASED ON FLC-GA Heru Suwoyo; Yingzhong Tian; Muhammad Hafizd Ibnu Hajar
SINERGI Vol 24, No 2 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.577 KB) | DOI: 10.22441/sinergi.2020.2.008

Abstract

Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.
A MAPAEKF-SLAM ALGORITHM WITH RECURSIVE MEAN AND COVARIANCE OF PROCESS AND MEASUREMENT NOISE STATISTIC Heru Suwoyo; Yingzhong Tian; Wenbin Wang; Md Musabbir Hossain; Long Li
SINERGI Vol 24, No 1 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (859.498 KB) | DOI: 10.22441/sinergi.2020.1.006

Abstract

The most popular filtering method used for solving a Simultaneous Localization and Mapping is the Extended Kalman Filter. Essentially, it requires prior stochastic knowledge both the process and measurement noise statistic. In order to avoid this requirement, these noise statistics have been defined at the beginning and kept to be fixed for the whole process. Indeed, it will satisfy the desired robustness in the case of simulation. Oppositely, due to the continuous uncertainty affected by the dynamic system under time integration, this manner is strongly not recommended. The reason is, improperly defined noise will not only degrade the filter performance but also might lead the filter to divergence condition. For this reason, there has been a strong manner well-termed as an adaptive-based strategy that commonly used to equip the classical filter for having an ability to approximate the noise statistic. Of course, by knowing the closely responsive noise statistic, the robustness and accuracy of an EKF can increase. However, most of the existed Adaptive-EKF only considered that the process and measurement noise statistic are characteristically zero-mean and responsive covariances. Accordingly, the robustness of EKF can still be enhanced. This paper presents a proposed method named as a MAPAEKF-SLAM algorithm used for solving the SLAM problem of a mobile robot, Turtlebot2. Sequentially, a classical EKF was estimated using Maximum a Posteriori. However, due to the existence of unobserved value, EKF was also smoothed one time based on the fixed-interval smoothing method. This smoothing step aims to keep-up the derivation process under MAP creation. Realistically, this proposed method was simulated and compared to the conventional one. Finally, it has been showing better accuracy in terms of Root Mean Square Error (RMSE) of both Estimated Map Coordinate (EMC) and Estimated Path Coordinate (EPC).       
AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT Heru Suwoyo; Yingzhong Tian; Muhammad Hafizd Ibnu Hajar
SINERGI Vol 24, No 3 (2020)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2020.3.002

Abstract

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.
The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm Heru Suwoyo; Muhammad Hafizd Ibnu Hajar; Prastika Indriyanti; Arafat Febriandirza
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.003

Abstract

The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.
Co-Authors Abdurohman Abdurohman Adriansyah, Andi Andi Adriansyah Andi Adriansyah Andi Adriansyah Andi Adriansyah Andi Adriansyah Chenwei Deng Deng, Chenwei Fahrudin Fahrudin Fajri Rezki Hutomo Febriandirza, Arafat Fengfeng Xi Ferryawan Harris Kristanto Guangjie Yuan Hossain, Md Musabbir Ibnu Hajar, Muhammad Hafizd Julpri Andika Julpri Andika Ketty Siti Salamah Li, Long Long Li Long Li Md Musabbir Hossain Muhammad Hafizd Ibnu Hajar Muhammad Hafizd Ibnu Hajar Muhammad Hafizd Ibnu Hajar Muhammad Hafizd Ibnu Hajar Muhammad Hafizd Ibnu Hajar Muhammad Hafizd Ibnu Hajar Muhammad Yusuf Prastika Indriyanti Rachmat Muwardi Raudah Alfiani Taufik Hidayat Thathit Gumilar Triwidya Mochtar Thong, Zhou Tian, Yingzhong Tian, Yingzhong Tong Zhou Triyanto Pangaribowo Wang, Wenbin Wenbin Wang Wenbin Wang Yifan Li Yingzhong Tian Yingzhong Tian Yingzhong Tian Yingzhong Tian Yingzhong Tian Yudhi Gunardi Zendi Iklima Zhou Thong