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Journal of Robotics and Control (JRC)
Published by Universitas Muhammadiyah Yogyakarta
ISSN : 27155056     EISSN : 27155072     DOI : https://doi.org/10.18196/jrc
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering
Journal of Robotics and Control (JRC) is an international open-access journal published by Universitas Muhammadiyah Yogyakarta. The journal invites students, researchers, and engineers to contribute to the development of theoretical and practice-oriented theories of Robotics and Control. Its scope includes (but not limited) to the following: Manipulator Robot, Mobile Robot, Flying Robot, Autonomous Robot, Automation Control, Programmable Logic Controller (PLC), SCADA, DCS, Wonderware, Industrial Robot, Robot Controller, Classical Control, Modern Control, Feedback Control, PID Controller, Fuzzy Logic Controller, State Feedback Controller, Neural Network Control, Linear Control, Optimal Control, Nonlinear Control, Robust Control, Adaptive Control, Geometry Control, Visual Control, Tracking Control, Artificial Intelligence, Power Electronic Control System, Grid Control, DC-DC Converter Control, Embedded Intelligence, Network Control System, Automatic Control and etc.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 15 Documents
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Search results for , issue "Vol 4, No 4 (2023)" : 15 Documents clear
Optimum PID Controller with Fuzzy Self-Tuning for DC Servo Motor Abdelghany, M. A.; Okasha Elnady, Abdelrady; Ibrahim, Shorouk Ossama
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18676

Abstract

DC motors are simple and controllable, making them a popular choice for various applications. However, the speed and load characteristics of DC motors can change, making it difficult to control them effectively. This paper proposes an optimum PID controller with fuzzy self-tuning for DC servo motors. The controller uses two steps to adjust the PID gains: The ACS algorithm is employed to identify the optimal PID gains in the first step. A fuzzy logic (FLC) controller is employed in the second stage to further fine-tune the gains. The FLC considers two cost functions: the first function is the sum of the squares of the error between the controlled output and reference input. The second function is a mathematical expression that specifies the required characteristics of the system response. The fuzzy self-tune then uses a set of rules to adjust the PID gains in response to changes in the system. The rules are based on the two cost functions designed to maintain the optimum PID gains for various operating settings. The outcomes of the two functions are: Kp = 5.2381, Ki = 7.0427, and Kd = 0.49468, with rising time = 0.2503, overshoot = 2.5079, and settling time = 10.4824 in the first cost function. The second cost function outcomes are Kp = 8.1381; Ki = 8.6427; and Kd = 0.49468. The FST-PID controller's performance is evaluated using Matlab-Simulink. The proposed controller was tested on a DC servo motor, and the results showed good performance in both steady-state and transient responses. The controller also maintained the optimum PID gains in the event of changes or disturbances. So, the motor's speed can effectively control under a variety of conditions.
Nonlinear Cascaded Control for a DC-DC Boost Converter Mansouri, Amirhosein; Gavagsaz-Ghoachani, Roghayeh; Phattanasak, Matheepot; Pierfederici, Serge
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18932

Abstract

The Boost Converter is a type of DC-DC converter that operates using switching techniques and is designed to elevate the voltage level. This paper presents a cascaded control for a boost converter to ensure that the inductor current and output capacitor voltage remain in a safe operating zone. Ensuring safe operating conditions and stable closed-loop poles is crucial because it guarantees that both current and voltage remain within the designated operating range. This preventive measure prevents any damage to components like capacitors (C), inductors (L), and switches. Unstable operation, on the other hand, could lead to oscillations and an undesirable increase in the amplitude of current and voltage, posing a risk to all components involved. The research contribution involves an investigation of cascaded control, utilizing power and energy concepts due to their advantageous effects on system performance and design. By implementing nonlinear controllers based on a large-signal averaged model, the closed-loop poles remain independent of operating points, eliminating the need for small-signal linearization. Small-signal linearization makes the controlled system dependent on the operating point. Two controllers are introduced based on power and energy concept, which is easy to understand. The potential practical application of the proposed cascaded control approach is in high-power applications. Tracking the energy stored in the output capacitor is first investigated to validate the proposed control scheme by varying the output voltage reference from 32 V to 50 V. Then, the regulation of the energy voltage is explored by varying the load resistance for the output voltage at 50 V. Both are done using a switched model using MATLAB/Simulink software. Simulation results are given to demonstrate the effectiveness of the proposed method. The key metrics used to assess the effectiveness of the proposed control scheme are the undershoot voltage and robustness. The results show that the studied system's tracking, regulating operations and robustness properties are as expected. The proposed method faces a challenge with the number of sensors required. To address this, observers can be utilized to reduce sensor usage while maintaining measurement accuracy. The proposed method can be applied to other power electronic systems.
Implementation of IoT of an Electric Infant Warmer to Prevent Hypothermia in Newborns Fauzi, Ekha Rifki; Maharesi, Angger; Setiyadi, Noor Alis
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18539

Abstract

Hypothermia is a drop in body temperature below 36.5°C in newborns. It results in an internal distribution of body heat from the nucleus to the periphery, followed by heat loss greater than metabolic production. Hypothermia is one of the factors predisposing to metabolic disorders, intracranial hemorrhage, respiratory distress, and Necrotizing enterocolitis. Hypothermia problems can be treated with infant warmers. Thus, the need for a infant warmer is considered to improve survival in newborns. This study aims to improve the accuracy of temperature monitoring, increase security, and enable remote monitoring. The temperature sensor of the device is calibrated with comparable devices such as Incubator Analyzer and Thermo hygrometer while the SpO2 sensor is calibrated with Spotlight SpO2 Functional Tester and Thermo hygrometer. Achievement and validation of temperature and oxygen saturation use a calibration comparison tool. The results of the temperature sensor measurements, including air temperature and skin sensor temperature, namely: air temperature error tolerance ≤2°C and skin sensor temperature error tolerance ± 0.5 ° C. All two indicators have the same standard deviation value of ±0.49. The SpO2 indicator reached an error tolerance value of ± 1% O2 with a standard deviation value of ± 0.6-0.9 from six trials. Then the pulse rate indicator obtained an error tolerance of ±5% with a standard deviation value of ±0.6. The smart infant warmer tool provides benefits to avoid excessive heat from the heater and minimize low temperatures that cause hypothermia through the Internet of Things technology. Furthermore, this research can be improved with machine learning technology to increase efficiency and effectiveness in patient treatment.
Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton Karis, Mohd Safirin; Kasdirin, Hyreil Anuar; Abas, Norafizah; Saad, Wira Hidayat Mohd; Zainudin, Muhammad Noorazlan Shah; Ali, Nursabilillah Mohd; Aras, Mohd Shahrieel Mohd
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.19299

Abstract

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.
An Application of Modified T2FHC Algorithm in Two-Link Robot Controller Ngo, Thanh Quyen; Tran, Thanh Hai; Le, Tong Tan Hoa; Lam, Binh Minh
Journal of Robotics and Control (JRC) Vol 4, No 4 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i4.18943

Abstract

Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.

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