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All Journal TEKNIK Mechatronics, Electrical Power, and Vehicular Technology Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Automotive Experiences Jurnal Rekayasa elektrika
Endra Joelianto
Instrumentation and Control Research Group Department of Engineering Physics Bandung Institute of Technology, Bandung 40132, Indonesia
Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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Journal : Mechatronics, Electrical Power, and Vehicular Technology

 1 
Modeling, Identification, Estimation, and Simulation of Urban Traffic Flow in Jakarta and Bandung Sutarto, Herman Y.; Joelianto, Endra
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 1 (2015)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4365.708 KB) | DOI: 10.14203/j.mev.2015.v6.57-66

Abstract

This paper presents an overview of urban traffic flow from the perspective of system theory and stochastic control. The topics of modeling, identification, estimation and simulation techniques are evaluated and validated using actual traffic flow data from the city of Jakarta and Bandung, Indonesia, and synthetic data generated from traffic micro-simulator VISSIM. The results on particle filter (PF) based state estimation and Expectation-Maximization (EM) based parameter estimation (identification) confirm the proposed model gives satisfactory results that capture the variation of urban traffic flow. The combination of the technique and the simulator platform assembles possibility to develop a real-time traffic light controller.  
Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm Joelianto, Endra; Christian, Daniel; Samsi, Agus
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 11, No 1 (2020)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2020.v11.1-10

Abstract

Unmanned aerial vehicle (UAV) quadrotors have developed rapidly and continue to advance together with the development of new supporting technologies. However, the use of one quadrotor has many obstacles and compromises the ability of a UAV to complete complex missions that require the cooperation of more than one quadrotor. In nature, one interesting phenomenon is the behaviour of several organisms to always move in flocks (swarm), which allows them to find food more quickly and sustain life compared with when they move independently. In this paper, the swarm behaviour is applied to drive a system consisting of six UAV quadrotors as agents for flocking while tracking a swarm trajectory. The swarm control system is expected to minimize the objective function of the energy used and tracking errors. The considered swarm control system consists of two levels. The first higher level is a proportional – derivative type controller that produces the swarm trajectory to be followed by UAV quadrotor agents in swarming. In the second lower level, a linear quadratic regulator (LQR) is used by each UAV quadrotor agent to follow a tracking path well with the minimal objective function. A genetic algorithm is applied to find the optimal LQR weighting matrices as it is able to solve complex optimization problems. Simulation results indicate that the quadrotors' tracking performance improved by 36.00 %, whereas their swarming performance improved by 17.17 %.
Modeling, Identification, Estimation, and Simulation of Urban Traffic Flow in Jakarta and Bandung Herman Y. Sutarto; Endra Joelianto
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 6, No 1 (2015)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2015.v6.57-66

Abstract

This paper presents an overview of urban traffic flow from the perspective of system theory and stochastic control. The topics of modeling, identification, estimation and simulation techniques are evaluated and validated using actual traffic flow data from the city of Jakarta and Bandung, Indonesia, and synthetic data generated from traffic micro-simulator VISSIM. The results on particle filter (PF) based state estimation and Expectation-Maximization (EM) based parameter estimation (identification) confirm the proposed model gives satisfactory results that capture the variation of urban traffic flow. The combination of the technique and the simulator platform assembles possibility to develop a real-time traffic light controller.  
Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm Endra Joelianto; Daniel Christian; Agus Samsi
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 11, No 1 (2020)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2020.v11.1-10

Abstract

Unmanned aerial vehicle (UAV) quadrotors have developed rapidly and continue to advance together with the development of new supporting technologies. However, the use of one quadrotor has many obstacles and compromises the ability of a UAV to complete complex missions that require the cooperation of more than one quadrotor. In nature, one interesting phenomenon is the behaviour of several organisms to always move in flocks (swarm), which allows them to find food more quickly and sustain life compared with when they move independently. In this paper, the swarm behaviour is applied to drive a system consisting of six UAV quadrotors as agents for flocking while tracking a swarm trajectory. The swarm control system is expected to minimize the objective function of the energy used and tracking errors. The considered swarm control system consists of two levels. The first higher level is a proportional – derivative type controller that produces the swarm trajectory to be followed by UAV quadrotor agents in swarming. In the second lower level, a linear quadratic regulator (LQR) is used by each UAV quadrotor agent to follow a tracking path well with the minimal objective function. A genetic algorithm is applied to find the optimal LQR weighting matrices as it is able to solve complex optimization problems. Simulation results indicate that the quadrotors' tracking performance improved by 36.00 %, whereas their swarming performance improved by 17.17 %.
Swarm control of an unmanned quadrotor model with LQR weighting matrix optimization using genetic algorithm Endra Joelianto; Daniel Christian; Agus Samsi
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 11, No 1 (2020)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2020.v11.1-10

Abstract

Unmanned aerial vehicle (UAV) quadrotors have developed rapidly and continue to advance together with the development of new supporting technologies. However, the use of one quadrotor has many obstacles and compromises the ability of a UAV to complete complex missions that require the cooperation of more than one quadrotor. In nature, one interesting phenomenon is the behaviour of several organisms to always move in flocks (swarm), which allows them to find food more quickly and sustain life compared with when they move independently. In this paper, the swarm behaviour is applied to drive a system consisting of six UAV quadrotors as agents for flocking while tracking a swarm trajectory. The swarm control system is expected to minimize the objective function of the energy used and tracking errors. The considered swarm control system consists of two levels. The first higher level is a proportional – derivative type controller that produces the swarm trajectory to be followed by UAV quadrotor agents in swarming. In the second lower level, a linear quadratic regulator (LQR) is used by each UAV quadrotor agent to follow a tracking path well with the minimal objective function. A genetic algorithm is applied to find the optimal LQR weighting matrices as it is able to solve complex optimization problems. Simulation results indicate that the quadrotors' tracking performance improved by 36.00 %, whereas their swarming performance improved by 17.17 %.
Failure assessment in lithium-ion battery packs in electric vehicles using the failure modes and effects analysis (FMEA) approach Rizky Cahya Kirana; Nicco Avinta Purwanto; Nadana Ayzah Azis; Endra Joelianto; Sigit Puji Santosa; Bentang Arief Budiman; Le Hoa Nguyen; Arjon Turnip
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 1 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.94-104

Abstract

The use of batteries in electric cars comes with inherent risks. As the crucial component of these vehicles, batteries must possess a highly dependable safety system to ensure the safety of users. To establish such a reliable safety system, a comprehensive analysis of potential battery failures is carried out. This research examines various failure modes and their effects, investigates the causes behind them, and quantifies the associated risks. The failure modes and effect analysis (FMEA) method is employed to classify these failures based on priority numbers. By studying 28 accident reports involving electric vehicles, data is collected to identify potential failure modes and evaluate their risks. The results obtained from the FMEA assessment are used to propose safety measures, considering the importance of the potential failure modes as indicated by their risk priority number (RPN). The design incorporates safeguards against mechanical stress, external short circuits, and thermal runaway incidents. The findings of this study enhance our understanding of electric vehicle (EV) battery safety and offer valuable insights to EV manufacturers, regulators, and policymakers, aiding them in the development of safer and more reliable electric vehicles.
Co-Authors Aditya Rio Prabowo Agus Samsi Anna Antonyova Anna Antonyova, Anna Arjon Turnip Arjon Turnip Bentang Arief Budiman Dananjaya, Alex Daniel Christian daniel christian Eko Mursito Budi Fuady Ramdhani Hanandita, Hanif Setya Herman Y. Sutarto Herman Y. Sutarto Le Hoa Nguyen Lenggana, Bhre Wangsa Nadana Ayzah Azis Nicco Avinta Purwanto oberli tansri Parlindungan H. Sitanggang Parlindungan H. Sitanggang Peter Antony Peter Antony, Peter Rajasa, Winarendra Satya Rizky Cahya Kirana Samsi, Agus Santosa, Sigit Puji Ubaidillah Ubaidillah