Goal-seeking Behavior-based Mobile Robot Using Particle Swarm Fuzzy Controller

Adriansyah, Andi; Gunardi, Yudhi; Badaruddin, Badaruddin; Ihsanto, Eko

doi:10.12928/telkomnika.v13i2.1111

Cited by 20 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the end position that reached by the robot and the total number of errors expressed by (4) have been observed. The position achievement of the robot with PID-GA controller farther from the starting position.…”

Section: Fig 3 Wall-following Processmentioning

confidence: 99%

“…Besides, these distances are processed to generate the proper movement with the involvement of a specific controller. The success of this robot lies in its capability to follow the predetermined path by concerning quickness and accuracy, that apply in automatically wheelchair, electronic goods transportation, and automated guided vehicle (AVG) [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller

Suwoyo¹,

Tian²,

Deng³

et al. 2018

EECSI

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Fig 3 Wall-following Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller

Suwoyo¹,

Tian²,

Deng³

et al. 2018

EECSI

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is also research which used multiple regression for prediction of average speed (Bagus & Azlina, 2017). Other research added fuzzy (Adriansyah, Gunardi, Badaruddin, & Ihsanto, 2015) to estimate real-time traffic volume (Dai & Yang, 2006), and to forecast rainfall (Othman & Azahari, 2016). Another research used feature extraction in prediction (Fitrianah, 2015).…”

Section: Related Workmentioning

confidence: 99%

Traffic Flow Prediction Model Based on Neighbouring Roads Using Neural Network and Multiple Regression

Priambodo

Ahmad

2018

JICT

View full text Add to dashboard Cite

Monitoring and understanding traffic congestion seems difficult due to its complex nature. This is because the occurrence of traffic congestion is dynamic and interrelated and it depends on many factors. Traffic congestion can also propagate from one road to neighbouring roads. Recent research shows that there is a spatial correlation between neighbouring roads with different traffic flow pattern on weekdays and on weekends. Previously, prediction of traffic flow propagation was based on day and time during weekdays and on weekends. Results obtained from past studies show that further investigation is needed to reduce errors using a more efficient method. We observed from previous research that similarity of traffic condition on weekdays and weekends was not taken into account in predicting traffic flow propagation. Hence, our study is to create and evaluate a new prediction model for traffic flow propagation at neighbouring roads using similarity of traffic flow pattern on weekdays and weekends to achieve more accurate results. We exploit similarity of traffic flow pattern on weekdays and weekends by adding

show abstract

“…149 the advantage that it does not require an accurate mathematical model of the system [14,15]. Hence, fuzzy adaptive PID control was developed to utilize the advantages of both PID control and fuzzy logic control.…”

Section: Co-simulation and Experiments Research On A Novel Erection Mementioning

confidence: 99%

Co-simulation and Experiment Research on a Novel Erection Mechanism

Jiangtao¹,

Huang²,

Gao³

et al. 2016

TELKOMNIKA

View full text Add to dashboard Cite

The erection mechanism with movable back hinged bearing is a novel erection mechanism and the form of its moving process is complicated. The novel erection mechanism needs to be extensively tested to prove its value and to ensure it works properly. Kinematic Keywords: Erection mechanism, Co-simulation, Virtual prototype technology, Fuzzy controlCopyright © 2016 Universitas Ahmad Dahlan. All rights reserved. IntroductionThe erection mechanism with movable back hinged bearing is a novel erection mechanism. Compared with traditional erection mechanism it adds horizontal cylinder, therefore back hinged bearing can move in horizontal direction driven by hydraulic cylinder. The novel mechanism can fulfill the erection requirements in strictured space. The erection mechanism needs to be extensively tested to prove its value and to ensure it works properly.Experiments normally require expensive equipment and long process of part design and system integration. Comprehensive simulations are often necessary and helpful. The erection mechanism contains mechanical and hydraulic system, therefore alone software cannot completely achieve its feature. The dynamic behavior of hydraulic system is highly non-linear due to the phenomena such as nonlinearities of valve cylinder combination, friction, fluid compressibility and associated stiffness, which cause difficulties in the control of such systems.Virtual prototype technology provides an effective approach for investigating the novel erection mechanism. One widely used simulation software is Simulink, which is a diagram programming method and provides a general environment. ADAMS is a powerful mechanical design and simulation software, which provides tools to build mechanical structures and 3D visual simulation. AMESim offers a graphical modeling approach and many libraries of components particularly in hydraulics. Co-simulation method has been extensively adopted in research and simulation of hydraulic steel-belt overwind buffer device [1], braking performance of a vehicle [2], a two-axis tracking system [3], four-wheel-drive hybrid electric vehicle [4], a turbojet fuel system [5], parallel hybrid loader [6], an electric bus with motorized wheels [7], the composite ABS control of vehicles [8], and so on. The co-simulation platform exploits the advantages of different software. The simulation results illustrate that virtual prototype technology has a good application in many scopes and is an effective approach for investigating behaviors of complex systems. It can also save time and cost. Fuzzy logic control is an alternative approach to achieve desired goals. Chen C. Y. [9] proposed an integrated fuzzy controller to achieve a synchronous positioning objective for a dual-cylinder electro-hydraulic lifting system with unbalanced loadings, system uncertainties and disturbances. The experimental results showed that the controller can effectively achieve the objective of position synchronization. Li L [10] proposed a fuzzy adaptive sliding mode control scheme which

show abstract

Goal-seeking Behavior-based Mobile Robot Using Particle Swarm Fuzzy Controller

Abstract: Abstract

Cited by 20 publications

References 16 publications

Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller

Improving a Wall-Following Robot Performance with a PID-Genetic Algorithm Controller

Traffic Flow Prediction Model Based on Neighbouring Roads Using Neural Network and Multiple Regression

Co-simulation and Experiment Research on a Novel Erection Mechanism

Contact Info

Product

Resources

About