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All Journal Bulletin of Electrical Engineering and Informatics Emerging Science Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Journal of Soft Computing Exploration International Journal of Robotics and Control Systems Signal and Image Processing Letters
Suwarno, Iswanto
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Self-Motion Control Exoskeleton for Upper Limb Rehabilitation with Perceptron Neuron Motion Capture Osman, Mohamad Afwan; Azlan, Norsinnira Zainul; Suwarno, Iswanto; Samewoi, Abdul Rahman; Kamarudzaman, Nohaslinda
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1030

Abstract

Upper limb rehabilitation robot can facilitate patients to regain their original impaired arm function and reduce therapist’ workload. However, the patient does not have a direct control over his/ her arm movement, which may lead to discomfort or even injury. This paper focuses on the development of a self-motion rehabilitation robot using Perception Neuron motion capture, where the movement of the impaired arm imitates the motion of the healthy limb.  The Axis Neuron software receives the healthy upper limb’s motion data from Perception Neuron. Unity serves as the simulation engine software that provides a 3-dimensional animation. ARDUnity acts as the communication platform between Unity software with Arduino. Arduino code is generated using Wire Editor, which avoids the need of the programming to be written in C++ or C#. Finally, Arduino instructs the exoskeleton motors that are connected to the impaired arm to move, following the healthy joint’s motion. The forward kinematics analysis for the robotic exoskeleton has been carried out to identify its workspace. Hardware experimental tests on the elbow and wrist flexion/ extension have shown the root-mean-square errors (RMSE) between the healthy and impaired arms movement to be 1.5809○ and 12.1955○ respectively. The average time delay between the healthy and impaired elbow movement is 0.1 seconds. For the wrist motion, the time delay is 1 second. The experimental results verified the feasibility and effectiveness of the Perception Neuron in realizing the self-motion control robot for upper limb rehabilitation. The proposed system enables the patients to conduct the rehabilitation therapy in a safer and more comfortable way as they can directly adjust the speed or stop the movement of the affected limb whenever they feel pain or discomfort.
Selection and Evaluation of Robotic Arm based Conveyor Belts (RACBs) Motions: NARMA(L2)-FO(ANFIS)PD-I based Jaya Optimization Algorithm Fadhil Mohammed, Abdullah; Basil, Noorulden; Abdulmaged, Riyam Bassim; Marhoon, Hamzah M.; Ridha, Hussein Mohammed; Ma'arif, Alfian; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1243

Abstract

Scholars worldwide have shown considerable interest in the industrial sector, mainly due to its abundant resources, which have facilitated the adoption of conveyor belt technologies like Robotic Arm-Based Conveyor Belts (RACBs). RACBs rely on four primary movements: (i.e., joint, motor, gear, and sensor), which can have a significant impact on the overall motions and motion estimation. To optimize these operations, an assistive algorithm has been developed to enhance the effectiveness of motion by achieving favorable gains. However, each motion requires specific criteria for Fractional Order Proportional Integral Derivative (FOPID) controller gains, leading to various challenges. These challenges include the existence of multiple evaluation and selection criteria, the significance of these criteria for each motion, the trade-off between criterion performance for each motion, and determining critical values for the criteria. As a result, the evaluation and selection of the Proposed Jaya optimization algorithm for RACB motion control becomes a complex problem. To address these challenges, this study proposes a novel integrated approach for selecting the Jaya optimization algorithm in different RACB motions. The approach incorporates two evaluation methods: the Nonlinear Autoregressive Moving Average with exogenous inputs (NARMA-L2) controller for Neural Network (NN) weighting of the criteria, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for selecting the Jaya optimization algorithm. The approach consists of three main phases: RACB-based NARMA-L2 Controller Identification and Pre-processing, Development of NARMA-L2 controller-based NARMA(L2)-FO(ANFIS)PD-I, and Evaluation of FOPID criteria based on JOA. The proposed approach is evaluated based on NARMA(L2)-FO(ANFIS)PD-I that given 0.4074, 0.3156, 0.3724, 0.1898 and 0.2135 for K_p_joint, K_i_motor, K_d_sensor, λ_gear, and µ_N respectively, which verifies the soundness of the proposed methodology.
Function Approximation Technique-based Adaptive Force-Tracking Impedance Control for Unknown Environment Azlan, Norsinnira Zainul; Yamaura, Hiroshi; Suwarno, Iswanto
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1029

Abstract

An accurate force-tracking in various applications may not be achieved without a complete knowledge of the environment parameters in the force-tracking impedance control strategy. Adaptive control law is one of the methods that is capable of compensating parameter uncertainties. However, the direct application of this technique is only effective for time-invariant unknown parameters. This paper presents a Function Approximation Technique (FAT)-based adaptive impedance control to overcome uncertainties in the environment stiffness and location with consideration of the approximation error in the FAT representation. The target impedance for the control law have been derived for unknown time-varying environment location and constant or time-varying environment stiffness using Fourier Series. This allows the update law to be derived easily based on Lyapunov stability method. The update law is formulated based on the force error feedback. Simulation results in MATLAB environment have verified the effectiveness of the developed control strategy in exerting the desired amount of force on the environment in x-direction, while precisely follows the required trajectory along y-direction, despite the constant or time-varying uncertainties in the environment stiffness and location. The maximum force error for all unknown environment tested has been found to be less than 0.1 N. The test outcomes for various initial assumption of unknown stiffness between 20000N/m to 120000N/m have shown consistent and excellent force tracking. It is also evident from the simulation results that the proposed controller is effective in tracking time-varying desired force under the limited knowledge of the environment stiffness and location.
Current trend in control of artificial intelligence for health robotic manipulator Suwarno, Iswanto; Cakan, Abdullah; Raharja, Nia Maharani; Baballe, Muhammad Ahmad; Mahmoud, Magdi S.
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.96

Abstract

The increasing utilization of artificial intelligence and robots in various services in healthcare makes robots as preferred intelligent agent model. Robotic evolution produces the optimal robotic innovation in the robotic system or its subsystems, morphology, kinematics, and control. An intelligent algorithm is programmed into the control of the robotic manipulator. This paper aims to identify the control of artificial intelligence and identify comparisons of artificial intelligence algorithms control for healthcare robotic manipulators. This study uses a systematic literature review using the Preferred Reporting Items for Systematic Review (PRISMA). The potential for further articles is explored related to the theme of the research carried out. The conclusion obtained many studies have been carried out to optimize the work and tasks of the robotic arm manipulator, specifically developing various types of manipulator control (algorithms) combined with neural networks to get the right and appropriate algorithm.
Fuzzy logic method for making push notifications on monitoring system of IoT-based electric truck charging Al Madani Kurniawan, Aqsha; Khaula Amifia, Lora; Iskandar Riansyah, Moch.; Furizal, Furizal; Suwarno, Iswanto; Ma’arif, Alfian; Maghfiroh, Hari
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7412

Abstract

To minimize the negligence when charging electric vehicles, it is deemed important to have an internet of things (IoT) based monitoring system using a notification feature. The monitoring system of electric vehicle battery charging used a voltage divider and temperature sensor (DS18B20) installed on the Arduino Mega 2560 microcontroller with the addition of an ESP8266 Wi-Fi module for sending microcontroller data into the Blynk platform. A notification feature was added as the reminder that the battery has been overcharging or overheating. This study applied the Mamdani fuzzy logic method to determine the conditions when notifications must appear. The results of the application of the Mamdani fuzzy logic method were able to determine the conditions for push notifications to appear using the parameters as desired; by so doing, it is possible to create a battery monitoring system with accurate push notification feature to prevent the battery from being overcharged and overheated.
Performance Optimization of a DFIG-based Variable Speed Wind Turbines by IVC-ANFIS Controller Ouhssain, Said; Chojaa, Hamid; Aljarhizi, Yahya; Al Ibrahmi, Elmehdi; Hadoune, Aziz; Maarif, Alfian; Suwarno, Iswanto; Mossa, Mahmoud A.
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22118

Abstract

An improved indirect vector control (IVC) method for a wind energy conversion system (WECS) is presented in this research. Field-oriented control or indirect vector control as it is sometimes called is a very important element of contemporary WECS that employs DFIGs. This control strategy is pivotal for achieving high performance and efficiency of DFIG-based wind turbines because it offers direct control on the torque and power ratings of the generator. A doubly fed induction generator (DFIG) is used by the WECS to inject power to the grid. An adaptive network-based fuzzy inference system (ANFIS), which is proposed to replace traditional methods like linear PI controllers, is the basis for this IVC. In this paper we chose ANFIS controller over traditional linear Proportional-Integral (PI) controllers due to its ability to adapt and learn from the system, leading to improved performance. The rotor voltage is controlled by the proposed IVC in order to regulate the exchanged active and reactive power between the stator and the grid. In order to verify the proposed control in terms of performance and robustness, a comparative analysis between the proposed ANFIS and linear PI controllers for the WECS-DFIG system is performed by a simulation study in a MATLAB/Simulink environment. This analysis covers both the transient and steady states of operation. As a result, the proposed ANFIS controller shows improved efficiency and robustness compared to the linear PI controllers. This superiority stems from its ability to integrate the flexibility and effectiveness inherent in diverse artificial intelligence controllers, specifically the synergistic use of Neural Network (NN) and Fuzzy Logic (FL) algorithms. The ANFIS controller's adaptability to diverse operating conditions and its capability to learn and optimize its performance play pivotal roles in enhancing its control capabilities within the WECS-DFIG system.
Stock Price Forecasting with Multivariate Time Series Long Short-Term Memory: A Deep Learning Approach Furizal, Furizal; Ritonga, Asdelina; Ma’arif, Alfian; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22460

Abstract

Stocks with their inherent complexity and dynamic nature influenced by a multitude of external and internal factors, play a crucial role in investment analysis and trend prediction. As financial instruments representing ownership in a company, stocks not only reflect the company's performance but are also affected by external factors such as economic conditions, political climates, and social changes. In a rapidly changing environment, investors and analysts continuously develop models and algorithms to aid in making timely and effective investment decisions. This study applies a Sequential model to predict stock data using a LSTM neural network. The model consists of a single hidden LSTM layer with 200 units. The LSTM layer, the core element of this model, enables it to capture temporal patterns and long-term relationships within the data. The training and testing data were divided into 80% for training and 20% for testing. The Adam optimizer was chosen to optimize the model's learning process, with a learning rate of 0.001. Dropout techniques were applied to reduce overfitting, with a dropout rate of 0.4, along with batch normalization and ReLU activation functions to enhance model performance. Additionally, callback mechanisms, including ReduceLROnPlateau and EarlyStopping, were used to optimize the training process and prevent overfitting. The model was evaluated using MAE and MSE metrics on training, testing, and future prediction data. The results indicate that the model achieved high accuracy, with an MAE of 0.0142 on the test data. However, future predictions showed higher MAE values, suggesting room for improvement in long-term forecasting. The model's ability to accurately predict future stock closing prices can assist investors in making informed investment decisions.
Enhancing Autonomous Navigation in GNSS-Denied Environment: Obstacle Avoidance Observability-Based Path Planning for ASLAM Elahian, Samaneh; Atashgah, Mohammad Ali Amiri; Suwarno, Iswanto
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23519

Abstract

Obstacle avoidance (OA) is necessary for any path planning in outdoor environment to prevent any collision with the obstacles in natural environment. In this paper, a quadrotor navigates using Active Simultaneously Localization and Mapping (ASLAM) in GNSS-denied outdoor environment. In ASLAM, the quadrotor path is defined using real-time Observability Based Path Planning (OBPP) method, autonomously. To prepare using of the OBPP in outdoor environment, it is necessary to add the ability of OA to it. So, the OA-OBPP method is introduced which defines the path based on terrestrial landmarks while preventing any collision with the obstacles. This approach is developed by redefining a dataset of in range landmarks while all of the landmark in the vicinity of the obstacles are removed from the in-range landmarks dataset.  To evaluate the performance of the proposed method, simulations of the OA-OBPP algorithm are conducted for a simplified 6-Degree of Freedom (DOF) quadrotor using MATLAB. The simulations evaluate the efficiency, accuracy and robustness of the proposed method. Results across various scenarios show that the method effectively avoids collisions with obstacles while simultaneously determining a path to the goal. Additionally, a comparison of the position estimation RMSE with Monte Carlo PP highlights the accuracy of the OA-OBPP. The robustness of the method, tested with varying initial positions, demonstrates its success in real-time path planning (PP) from any starting point to the destination without collisions. The results confirm that the OA-OBPP enhances the robot's capability to perform real-time, autonomous, and robust path planning in outdoor environments, even in the absence of GNSS signals, through visual navigation.
Induction Motor Speed Control Using PID Tuned by Particle Swarm Optimization Under Vector Control Maghfiroh, Hari; Sulistyo, Meiyanto Eko; Ma’arif, Alfian; Raharja, Nia Maharani; Suwarno, Iswanto; Wati, Dwi Ana Ratna; Baballe, Muhammad Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13112

Abstract

Induction motors (IMs) are widely used in industrial applications due to their cost-effectiveness, durability, and low maintenance requirements. This study investigates the speed control of an induction motor using vector control combined with a PID controller whose parameters are tuned via Particle Swarm Optimization (PID-PSO). A reduced-order small-signal state-space model is derived from a detailed nonlinear IM model to facilitate efficient controller tuning while maintaining fidelity to real-world behavior. The PID parameters are optimized using PSO, with the Integral of Absolute Error (IAE) selected as the objective function due to its ability to penalize long-duration deviations and reflect steady-state performance. The optimized PID controller is then validated on the full nonlinear IM model under speed and load variations. Simulation results demonstrate that PID-PSO significantly outperforms manually tuned PID control in terms of tracking accuracy, reducing IAE by 37.79% and 14.76% under speed and load variation conditions, respectively. However, this improvement comes at the cost of slightly slower settling time. These results highlight a trade-off between accuracy and transient response, motivating future research on multi-objective optimization to balance conflicting criteria such as robustness, energy efficiency, and response time.
Monitoring Tools of Water Temperature and pH in Fresh Water Fish Pool Based on the Internet of Things (IoT) Amin, Muhammad; Ma'arif, Alfian; Suwarno, Iswanto
Signal and Image Processing Letters Vol 4, No 3 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v4i2.51

Abstract

One of the fish species that can live in high salinity (euryhaline) is tilapia (Oreochromis niloticus). It is not surprising that tilapia can be found in brackish water, swamps, reservoirs, lakes and rivers. The growth rate of tilapia is influenced by age and water quality as well as the quality and quantity of feed given. The definition of growth is the increase in weight and also the length of the body of living things during a certain period of time. These parameters should be monitored for the survival of the fish. The use of Internet of Things (IoT) can be used as a monitoring and automation system for fish environmental parameters. IoT in this research was chosen because this system can simplify human work. The instruments needed in designing the monitoring system include components that function as the main controller, namely the Node MCU ESP2866 microcontroller, components for detecting water pH levels, namely a pH meter sensor type pH-4502C, and a water temperature measuring component, namely DS18B20. NodeMCU is the main component as a microcontroller as well as a component that connects to Wi-Fi. This system displays the temperature and pH of the water on the LCD and is sent to the android application. The results of the design of the temperature and pH monitoring tool obtained the average error value on temperature readings is 0.88%. The small error value gives the conclusion that the temperature sensor readings in this study are accurate. The pH acidity reading has an average error value of 0.42%. The small error value gives the conclusion that the pH sensor readings in this study are accurate. With the results of sensor value readings and errors, it can be concluded that the tool can be used and can work properly.
Co-Authors Abboud, Sarah Abdulmaged, Riyam Bassim Al Ibrahmi, Elmehdi Al Madani Kurniawan, Aqsha Aldi Bastiatul Fawait Fawait Alfian Ma’arif Aljarhizi, Yahya Ansarifard, Mehdi Asno Azzawagama Firdaus Atashgah, Mohammad Ali Amiri Azlan, Norsinnira Zainul Baballe, Muhammad Ahmad Basil, Noorulden Boulal, Abdellah Cakan, Abdullah Chojaa, Hamid Diab, Ahmed A. Zaki Dwi Ana Ratna Wati Elahian, Samaneh Elsawy, Ahmed M. Fadhil Mohammed, Abdullah Faikul Umam Fawzy, Ibram Y. Furizal Furizal, Furizal Habachi, Rachid Hadoune, Aziz Kamarudzaman, Nohaslinda Lora Khaula Amifia Loulijat, Azeddine Maghfiroh, Hari Maharani Raharja, Nia Mahmoud Zadehbagheri Mahmoud, Magdi S. Marhoon, Hamzah M. Meiyanto Eko Sulistyo Moch. Iskandar Riansyah Mossa, Mahmoud A. Muhammad Amin Muhammad Fuad Murni Murni Nia Maharani Raharja Nugraha, Ikhwan Osman, Mohamad Afwan Ouhssain, Said Puriyanto, Riky D. Rahim Ildarabadi Ridha, Hussein Mohammed Ritonga, Asdelina Samewoi, Abdul Rahman Semma, El Alami Sugiyarta Sugiyarta Wahyu Caesarendra Wijaya, Setiawan Ardi Yamaura, Hiroshi Yunandha, Isro D.