Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Journal of Robotics and Control (JRC)
Lam, Binh Minh
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Adaptive Single-Input Recurrent WCMAC-Based Supervisory Control for De-icing Robot Manipulator Ngo, Thanh Quyen; Le, Tong Tan Hoa; Lam, Binh Minh; Pham, Trung Kien
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.v4i4.18464

Abstract

The control of any robotic system always faces many great challenges in theory and practice. Because between theory and reality, there is always a huge difference in the uncertainty components in the system. That leads to the accuracy and stability of the system not being guaranteed with the set requirements. This paper presents a novel adaptive single-input recurrent wavelet differentiable cerebellar model articulation controller (S-RWCMAC)-based supervisory control system for an m-link robot manipulator to achieve precision trajectory tracking. This adaptive S-RWCMAC-based supervisory control system consists of a main adaptive S-RWCMAC, a supervisory controller, and an adaptive robust controller. The S-RWCMAC incorporates the advantages of the wavelet decomposition property with a CMAC fast learning ability, dynamic response, and input space dimension of RWCMAC can be simplified; and it is used to control the plant. The supervisory controller is appended to the adaptive S-RWCMAC to force the system states within a predefined constraint set and the adaptive robust controller is developed to dispel the effect of the approximate error. In this scheme, if the adaptive S-RWCMAC can not maintain the system states within the constraint set. Then, the supervisory controller will work to pull the states back to the constraint set and otherwise is idle. The online tuning laws of S-RWCMAC and the robust controller parameters are derived from the gradient-descent learning method and Lyapunov function so that the stability of the system can be guaranteed. The simulation and experimental results of the novel three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology. The results indicate that the proposed model has superior accuracy compared to that of the Standalone CMAC Controller. The parameters of the average squared error in the S-RWCMAC -based 3 robot joints are lower than those of the Standalone CMAC Controller by 0.023%, 0.029%, and 0.032%, respectively.
Research on the impact of sliding window and differencing procedures on the support vector regression model for load forecasting Tran, Thanh Ngoc; Dang, Thi Phuc; Lam, Binh Minh; Nguyen, Anh Tuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1314-1322

Abstract

Load forecasting is a critical aspect of energy management and grid operations. Machine learning techniques as support vector regression (SVR), have been widely used for load forecasting. However, the effectiveness of SVR is highly dependent on its hyperparameters, including the error sensitivity parameter, penalty factor, and kernel function. Furthermore, as the load data follows a time series pattern, the accuracy of the SVR model is influenced by the data's characteristics. In this regard, the present study aims to investigate the impact of combining the sliding window procedure and differencing the input data on the prediction accuracy of the SVR model. The study utilizes daily maximum load data from the Queensland and Victoria states in Australia. The analyses revealed that while the sliding window procedure had a minimal impact on the prediction results, the differencing of the input data significantly improved the accuracy of the predictions.
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.
Co-Authors Dang, Thi Phuc Le, Tong Tan Hoa Ngo, Thanh Quyen Nguyen, Anh Tuan Pham, Trung Kien Tran, Thanh Hai Tran, Thanh Ngoc