International Journal of Robotics and Control Systems
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
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<![CDATA[Wall Following Control System with PID Control and Ultrasonic Sensor for KRAI 2018 Robot ]]>
<![CDATA[Muhamad Hamam Iqbal]]>;
<![CDATA[Wahyu Sapto Aji]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.206
<![CDATA[Indonesian Abu Robot Contest (KRAI) in 2018 with the theme "Throwing a Blessing Ball". The main purpose of this robot is to be able to navigate automatically in an area that is bordered by walls to complete the mission. The main problem with the robot is the navigation system. The application of PID control in the wall following system has been able to make robot movements smooth, responsive, and fast. In this study, PID control aims to smooth the movement of the robot while walking along the wall in the race arena. The PID parameter is obtained from the results of tuning with the trial and error method, the values of KP = 3, KI = 0, and KD = 5. At the PWM 150 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 4.4. cm. At the PWM 200 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.4 cm. At the PWM 250 set point the value of the ultrasonic sensor distance reading to the object in the form of a wall with an average error of 0.8 cm. This error does not have an effect on the performance of the wall following system, because the system only uses the distance value reading data with a decimal value in front of the comma. So it can be concluded that the wall following system which is designed using ultrasonic sensors with measurement error that occurs is zero.]]>
<![CDATA[An Optimally Configured HP-GRU Model Using Hyperband for the Control of Wall Following Robot ]]>
<![CDATA[Abdul Rehman Khan]]>;
<![CDATA[Ameer Tamoor Khan]]>;
<![CDATA[Masood Salik]]>;
<![CDATA[Sunila Bakhsh]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.281
<![CDATA[In this paper, we presented an autonomous control framework for the wall following robot using an optimally configured Gated Recurrent Unit (GRU) model with the hyperband algorithm. GRU is popularly known for the time-series or sequence data, and it overcomes the vanishing gradient problem of RNN. GRU also consumes less memory and is computationally more efficient than LSTMs. The selection of hyper-parameters of the GRU model is a complex optimization problem with local minima. Usually, hyper-parameters are selected through hit and trial, which does not guarantee an optimal solution. To come around this problem, we used a hyperband algorithm for the selection of optimal parameters. It is an iterative method, which searches for the optimal configuration by discarding the least performing configurations on each iteration. The proposed HP-GRU model is used on a dataset of SCITOS G5 robots with 24 sensors mounted. The results show that HP-GRU has a mean accuracy of 0.9857 and a mean loss of 0.0810, and it is comparable with other deep learning algorithms.]]>
<![CDATA[Implementation of DC Motor PID Control On Conveyor for Separating Potato Seeds by Weight ]]>
<![CDATA[Daniya Sonny Febriyan]]>;
<![CDATA[Riky Dwi Puriyanto]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.221
<![CDATA[Dieng area is a mountainous area, dieng has land with high fertility levels so that they are increasingly high in the agricultural sector, especially potatoes. During the time, the technology for sorting potato seeds was still manual with humans, so it was less effective. The conveyor here is very useful as this device which will later work with loadcell as a weigher, and this device will be driven using a dc motor then the speed stabilization uses the PID algorithm with trial and error methods and this tool is supported using ultrasonic sensors and two servo sorting. This device works with 1 cycle, namely with 1 command with a value of KP 55, KI 20 and KD 0.001. The conveyor movement is quite stable with an average error value of 0.276 load cell with a standard deviation of 0.211877 with an achievement level of 80%.]]>
<![CDATA[Analysis of Hybrid Technique for Motion Planning of Humanoid NAO ]]>
<![CDATA[Abhishek Kumar Kashyap]]>;
<![CDATA[Dayal R. Parhi]]>;
<![CDATA[Anish Pandey]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.285
<![CDATA[The navigation of a humanoid robot is essential because it is the basic requirement of any assigned task. Singly used motion planning techniques may take a long path to reach the target and increase the computational cost. Therefore, in this article, a hybrid controller is employed in the humanoid NAO for motion planning assignment. The Eagle strategy (ES) with Ant colony optimization (ACO) is introduced in this article for evaluating precise steering angles for humanoid robots as they traverse a route from a reference point to a target point. This enables the robot to achieve its specific target more quickly by avoiding barriers and obtaining the minimal global direction. The hybridized ES-ACO approach is critical in determining precise steering angles to escape obstacles. The details of terrain are obtained using vision and ultrasonic sensors, which also include the barriers ranges to the ES as an input variable. The ES's input parameters are the barrier ranges from the NAO in front, left, and right directions, and the technique's output variable is the precise steering angle. The designed controller is tested in both a simulation and an experimental setting with a variety of obstacles. The outcomes of both simulation and experimental conditions are compared, and a strong correlation is identified in those with the fewest deviations.]]>
<![CDATA[The Path Direction Control System for Lanange Jagad Dance Robot Using the MPU6050 Gyroscope Sensor ]]>
<![CDATA[Ibnu Rifajar]]>;
<![CDATA[Abdul Fadlil]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.225
<![CDATA[The ability to walk straight on a dance robot is very important considering that in competitions, dance robots are required to be able to walk through several zones starting from the starting zone and ending with the closed zone. Therefore, a control system is needed in the Lanange Jagad dance robot so that the robot can control the direction of its walking motion and reduce errors in dance motion while walking on the dance robot. This control system uses a reading value based on the orientation of the rotating motion on the yaw angle axis on the MPU6050 gyroscope sensor which will later be used as a corrector for dance robots when performing various dance movements while walking in the competition arena. From the results of the overall test of the Lanange Jagad dance robot after adding the road direction control system, the percentage of the success rate in the battery power supply condition is 12 volts to 12.6 volts by 100% with the greater the battery power supply, the error in the robot's final angle average to The starting angle of the robot is getting smaller and the percentage of the success rate at the slope of the 0o to 4o race arena is 93.3%. With the tilted race arena, the error in the mean error of the robot's final angle to the starting angle of the robot is also greater, so it can be concluded that the robot can be controlled direction of walking and can walk straight to the finish in the closed zone.]]>
<![CDATA[Multibody Modeling and Balance Control of a Reaction Wheel Inverted Pendulum Using LQR Controller ]]>
<![CDATA[Ümit Önen]]>;
<![CDATA[Abdullah Çakan]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.296
<![CDATA[In this study, modeling and LQR control of a reaction wheel inverted pendulum system is described. The reaction wheel inverted pendulum model is created by using a 3D CAD platform and exported to Simscape Multibody. The multibody model is linearized to derive a state-space representation. A LQR (Linear-quadratic regulator) controller is designed and applied for balance control of the pendulum. The results show that deriving a state-space representation from multibody is an easy and effective way to model dynamic systems and balance control of the reaction wheel inverted pendulum is successfully achieved by LQR controller. Results are given in the form of graphics.]]>
<![CDATA[Robot Operating System (ROS) in Quadcopter Flying Robot Using Telemetry System ]]>
<![CDATA[Mohammad Iqbalul Faiq Hatta]]>;
<![CDATA[Nuryono Satya Widodo]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.247
<![CDATA[In this study implementing odometry using RVIZ on a quadcopter flying robot that uses the Pixhawk Cube firmware version 3.6.8 as the sub-controller. Then the Lenovo G400 laptop as the main-controller as well as the Ground Control Station using the ubuntu 16.04 Linux operating system. The ROS platform uses the Kinetic and MAVROS versions as a quadcopter platform package using MAVlink communication with the telemetry module. The odometry system was tested using Rviz as navigation for Quadcopter movements in carrying out movements that follow movement patterns in certain shapes and perform basic robot movements. Data were collected using a standard measuring instrument inclinometer as a measurement of the slope of the robot and visualization RVIZ as a visual display of the odometric robot. The results of the research obtained are that the flying robot can maneuver according to the shape on the RVIZ according to the movements carried out directly at the airport, as well as the effect of the roll angle on the quadcopter (negative left roll, positive right) and the pitch angle on the quadcopter (negative forward pitch, the pitch returns positive).]]>
<![CDATA[Autonomous Fuzzy Heading Control for a Multi-Wheeled Combat Vehicle ]]>
<![CDATA[A. N. Ouda]]>;
<![CDATA[Amr Mohamed]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.286
<![CDATA[This paper introduces the design and the implementation of a heading angle tracking controller using fuzzy logic for a scaled Autonomous Multi-Wheeled Combat Vehicle (AMWCV) to navigate in outdoor environments. The challenge of designing this control system is to control the steering of the front four wheels individually to obtain the correct heading angle of the vehicle. The main contribution of the paper can be summarized in the fact that it is designing four fuzzy controllers for navigation and tracking the desired heading angle while at the same time while controlling the steering of the front four wheels individually. The AMWCV is capable of forwarding and backward movement where all eight wheels are powered individually. The different heading angles are used and simulated using MATLAB software to evaluate the performances of the developed algorithms. In addition, the performance of the developed controllers is validated in the presence of noise and disturbance in order to evaluate the robustness of the controller's Simulation results show the performances and demonstrate that the developed controllers are effective in predicting the desired heading angle changes.]]>
<![CDATA[Tuning of PID Controller Parameters with Genetic Algorithm Method on DC Motor ]]>
<![CDATA[Eka Widya Suseno]]>;
<![CDATA[Alfian Ma'arif]]>
<![CDATA[ International Journal of Robotics and Control Systems Vol 1, No 1 (2021) ]]>
Publisher : <![CDATA[Association for Scientific Computing Electronics and Engineering (ASCEE) ]]>
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DOI: 10.31763/ijrcs.v1i1.249
<![CDATA[Proportional Integral Derivative (PID) controllers are used in general to control a system, for example a DC motor system. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (KP), Integral Gain (KI) and Derivative Gain (KD). In this case, the genetic algorithm method is used which can give better results in each iteration. Genetic algorithms are one of the smart methods inspired by the process of natural selection, the process that causes biological evolution, this concept is applied to tuning PID parameters. This research uses the Matlab simulation method and applies the simulation results to the DC motor hardware using the Arduino Uno. The genetic algorithm method gives a system that has a better steady time and a smaller maximum spike than the Trial and Error method. The test process produced the two best data with an overshoot value = 2, settling time = 13.5 and rise time of 2.7872 and the PID parameter value for mutation of 1 was KP = 3.7500; KI = 1.3184 and KD = 0.2051. Then the value of the best PID parameter on Crossover is 0.4, which is KP = 4.2090; KI = 1.2012 and KD = 0.2539 with an overshoot value = 2, settling time = 18 and rise time = 2.6462.]]>
