Rusdhianto Effendi Abdul Kadir
Institut Teknologi Sepuluh Nopember

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Ball and Beam Control using Adaptive PID based on Q-Learning Brilian Putra Amiruddin; Rusdhianto Effendi Abdul Kadir
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 1: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2063

Abstract

The ball and beam system is one of the most used systems for benchmarking the controller response because it has nonlinear and unstable characteristics. Furthermore, in line with the increasing of computation power availability and artificial intelligence research intensity, especially the reinforcement learning field, nowadays plenty of researchers are working on a learning control approach for controlling systems. Due to that, in this paper, the adaptive PID controller based on Q-Learning (Q-PID) was used to control the ball position on the ball and beam system. From the simulation result, Q-PID outperforms the conventional PID and heuristic PID controller technique with the swifter settling time and lower overshoot percentage.
Obstacle Tracking on Unmanned Surface Vehicle Using Kalman Filter Rusdhianto Effendi Abdul Kadir; Mochammad Sahal; Yusuf Bilfaqih; Zulkifli Hidayat; Gaung Jagad
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 5, No 2 (2021): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v5i2.157

Abstract

Unmanned Surface Vehicles (USV) are self-driving vehicles that operate on the water surface. In order to be operated autonomously, USV has a guidance system designed for path planning to reach its destination. The ability to detect obstacles in its paths is one of the important factors to plan a new path in order to avoid obstacles and reach its destination optimally. This research designed an obstacle tracking system which integrates USV perception sensors such as camera and Light Detection and Ranging (LiDaR) to gain information of the obstacle’s relative position in the surrounding environment to the ship. To improve the relative position estimation of the obstacles to the ship, Kalman filter is applied to reduce the measurements noises. The results of the system design are simulated using MATLAB software so that results can be analyzed to see the performance of the system design. Results obtained using the Kalman filter show 12% noise reduction. Keywords: filter kalman, obstacle tracking, unmanned surface vehicle.
Modeling and Control Unmanned Surface Vehicle (USV) with Hybrid Drivetrain Muhammad Arieb Salam; Rusdhianto Effendi Abdul Kadir; Nurlita Gamayanti
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 3, No 2 (2019): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25796216.v3.i2.86

Abstract

With the rapid development of unmanned surface vehicle (USV), the development of hybrid electric vehicles on USV is a technology that has a very important role to overcome the problem of how long the vehicle can be used. In hybrid electric vehicles (HEV), the main components consist of generators, electric motors, and batteries. Modeling component is intended to design models that fit on unmanned surface vehicle and represent the state of USV with hybrid drivetrain. This paper focuses on modeling USV with hybrid drivetrain. This vehicle uses a series configuration so that the generator act as a power generating system. The power system on USV was chosen using hybrid sources of DC generator and lithium-ion battery pack. The optimal size of DC generator and battery pack are very important to ensure that USV can work for a long period of time. This paper describes the simple characteristics of a DC generator with a PI controller and gain-scheduling PI controller which is accurate in generating 380V output voltage according to the initial plan to be built. We also described the model of permanent magnet dc motor as a driver in USV and described simulation about 200Ah 36V lithium-ion batteries. The development of this model will be represented in MATLAB/Simulink.Keywords: hybrid drivetrain, hybrid electric vehicle, period of time, unmanned surface vehicle.