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All Journal Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Kasdirin, Hyreil Anuar
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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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.
New lambda tuning approach of single input fuzzy logic using gradient descent algorithm and particle swarm optimization Zohedi, Fauzal Naim; Aras, Mohd Shahrieel Mohd; Kasdirin, Hyreil Anuar; Nordin, Nurdiana Binti
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 3: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i3.pp1344-1355

Abstract

Underwater remotely operated vehicle (ROV) is important in underwater industries as well as for safety purposes. It can dive deeper than humans and can replace humans in a hazardous underwater environment. ROV depth control is difficult due to the hydrodynamic of the ROV itself and the underwater environment. Overshoot in the depth control may cause damage to the ROV and its investigated location. This paper presenting a new tuning approach of single input fuzzy logic controller (SIFLC) with gradient descent algorithm (GDA) and particle swarm optimization (PSO) implementation for ROV depth control. The ROV was modeled using system identification to simulate the depth system. Proportional integral derivative (PID) controller was applied to the model as a basic controller. SIFLC was then implemented in three tuning approaches which are heuristic, GDA, and PSO. The output transient was simulated using MATLAB Simulink and the percent overshoot (OS), time rise (Tr), and settling time (Ts) of the systems without and with controllers were compared and analyzed. The result shows that SIFLC GDA output has the best transient result at 0.1021% (OS), 0.7992s (Tr), and 0.9790s (Ts).
Ensemble Voting Regressor for Enhanced Prediction in EMG-Based Prosthetic Wrist Control Karis, Mohd Safirin; Kasdirin, Hyreil Anuar; Abas, Norafizah; Zainudin, Muhammad Noorazlan Shah; Ali, Nursabilillah Mohd; Saad, Wira Hidayat Mohd; Razlan, Zarina
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26222

Abstract

Accurately capturing user motion intention is crucial for effective wrist control in myelectronic prosthetic hands. While various regression models have been explored to improve prediction performance, each presents specific limitations when used independently. This study proposes a novel ensemble learning approach that utilizes a Voting Regressor to combine the strengths of several regression models ANN, ANFIS, fuzzy logic, and their combinations (ANN-ANFIS, ANN-Fuzzy, ANFIS-Fuzzy, and ANN-ANFIS-Fuzzy) to improve predictive performance. Surface EMG signals were collected from the FCR and ECRL muscles at five contraction levels: 20%, 40%, 60%, 80%, and 100% MVC. These signals were used to predict wrist velocity, which was then validated using a SimMechanics based prosthetic hand model in MATLAB 2017a. The ensemble model outperformed all individual and combination models at four MVC levels; 20%, 40%, 60%, and 100%. However, at 80% MVC, a single model achieved superior performance. Based on the average performance gain at the four winning MVC levels, the ensemble method achieved an overall improvement of 11.38%. When applied to the prosthetic hand simulation, the ensemble model showed slight additional improvements in RMSE at each MVC level, highlighting the practical applicability of the approach. To assign optimal and objective weights to the contributing models, MCDM-WSM approach was applied. This method combined multiple evaluation metrics (RMSE, %NRMSE, MAE, R², and p-value) into a single composite score, leading to the final weighted regression equation: YVR-HG-wrist = (0.5163)YANN + (0.2367)YANFIS + (0.2470)YFuzzy. Furthermore, the ensemble model reduced reliance on additional control strategies such as PID tuning, as its improvements in RMSE were comparable to those typically achieved through PID-based compensation. These findings highlight the potential of a performance-weighted ensemble approach to provide more accurate, robust, and practical EMG-based prosthetic wrist control especially in real-time applications.
Co-Authors Abas, Norafizah Ali, Nursabilillah Mohd Aras, Mohd Shahrieel Mohd Karis, Mohd Safirin Nordin, Nurdiana Binti Razlan, Zarina Saad, Wira Hidayat Mohd Zainudin, Muhammad Noorazlan Shah Zohedi, Fauzal Naim