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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

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 FLCâ€™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 ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT Suwoyo, Heru; Thong, Zhou; Tian, Yingzhong; Adriansyah, Andi; Ibnu Hajar, Muhammad Hafizd
TEKNOKOM Vol. 5 No. 1 (2022): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.

Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm Heru Suwoyo; Yingzhong Tian; Wenbin Wang; Long Li; Andi Adriansyah; Fengfeng Xi; Guangjie Yuan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 1: February 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i1.16223

The smooth variable structure filter (ASVSF) has been relatively considered as a new robust predictor-corrector method for estimating the state. In order to effectively utilize it, an SVSF requires the accurate system model, and exact prior knowledge includes both the process and measurement noise statistic. Unfortunately, the system model is always inaccurate because of some considerations avoided at the beginning. Moreover, the small addictive noises are partially known or even unknown. Of course, this limitation can degrade the performance of SVSF or also lead to divergence condition. For this reason, it is proposed through this paper an adaptive smooth variable structure filter (ASVSF) by conditioning the probability density function of a measurementto the unknown parameters at one iteration. This proposed method is assumed to accomplish the localization and direct point-based observation task of a wheeled mobile robot, TurtleBot2. Finally, by realistically simulating it and comparing to a conventional method, the proposed method has been showing a better accuracy and stability in term of root mean square error (RMSE) of the estimated map coordinate (EMC) and estimated path coordinate (EPC).

Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller Heru Suwoyo; Yingzhong Tian; Chenwei Deng; Andi Adriansyah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

A wall-following robot needs a robust controller that navigate robot based on the specified distance from the wall. The usage of PID controller has been successfully minimizing the dynamic error of wall-following robot. However, a manual setting of three unknown parameters of PID-controller often precisely increase instability. Hence, recently there are many approaches to solve this issue. This paper presents an approach to obtaining those PID parameters automatically by utilizing the role of Genetic Algorithm. The proposed method was simulated using MATLAB and tested in a real robot. Based on several experiments results it has been showing the effectiveness of reducing the dynamic error of the wall-following robot.

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

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

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

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 ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT Heru Suwoyo; Zhou Thong; Yingzhong Tian; Andi Adriansyah; Muhammad Hafizd Ibnu Hajar
TEKNOKOM Vol. 5 No. 1 (2022): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.

The ACA-based PID Controller for Enhancing a Wheeled-Mobile Robot Heru Suwoyo; Yingzhong Tian; Andi Adriansyah; Muhammad Hafizd Ibnu Hajar; Tong Zhou
Journal FORTEI-JEERI Vol. 1 No. 2 (2020): FORTEI-JEERI
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia (FORTEI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46962/forteijeeri.v1i2.15

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE.

The MAPWE-Boosted AEKF with Recursive-Against-Iteration Noise Statistic for Feature-Based SLAM Algorithm Heru Suwoyo
Journal FORTEI-JEERI Vol. 2 No. 1 (2021): FORTEI-JEERI
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia (FORTEI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46962/forteijeeri.v2i1.22

The unknown noise statistic might degrade the Filter performance or even lead to filter divergence. Accordingly, to enhance the classical EKF to approximate the recursive process and measurement noise statistic, based on Maximum A Posteriori creation and Weighted Exponent (WE) as the divergence suppression method, abbreviated as MAPWE, an adaptive EKF is proposed through this paper. Moreover, the existence of simplification during estimating noise statistics under MAP creation might also degrade its quality. Thus, the suboptimal MAP solution was also estimated based on Weighted Exponent. Indeed, the time-varying noise statistic under this process seems strongly accurate. But the complexity of the measurement covariance might also diverge from its positive definite characteristic. Thus, aiming to prevent this condition, the additional divergence suppression method was also involved in correcting the error state covariance in the smoothing step. This improvement is then used as SLAM algorithm for a mobile robot. Comparing to the conventional methods, it is better in term of RMSE for the estimated path and estimated map.

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