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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Tran, Thanh Hai
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 1 
Robust Adaptive Tracking Control for Uncertain Five-Bar Parallel Robot Using Fuzzy CMAC in Order to Improve Accuracy Ngo, Thanh Quyen; Tran, Thanh Hai; Le, Tong Tan Hoa
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21742

Abstract

Parallel robot systems are increasingly important and widely applied due to their superior advantages such as high speed and accuracy. To improve the accuracy of these systems, recent research has focused on developing advanced control methods. However, this remains a significant challenge due to the complex mathematical model of parallel robots. This study introduces a control system based on a fuzzy cerebellar model articulation controller (FCMAC) to control parallel robots. The proposed control system includes FCMAC as the main tracking controller used to estimate the ideal control. A robust controller is employed to compensate for the error between FCMAC and the ideal controller. The parameters of FCMAC are adjusted online based on adaptive laws derived from Lyapunov functions. Finally, a five-bar parallel robot is selected to experiment with the FCMAC algorithm to demonstrate the effectiveness of the proposed controller. The results show that the accuracy of FCMAC is better than that of other algorithms.
Developing an Advanced Control System to Enhance Precision in Uncertain Conditions for Five-Bar Parallel Robot Through a Combination of Robust Adaptive Tracking Control Using CMAC Le, Tong Tan Hoa; Ngo, Thanh Quyen; Tran, Thanh Hai
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22188

Abstract

Parallel robot systems have become increasingly applied due to significant advantages such as fast operating speed and high accuracy. Researchers are currently focusing on developing advanced control methods to increase the accuracy of this system. However, these advances face many challenges, including system dynamics and uncertain components in impact factors. Therefore, achieving a high level of accuracy remains a challenging problem and requires continued effort and careful research. This study proposes to use the Cerebellar Model Articulation Controller (CMAC) to estimate the nonlinear components of the system. By applying Lyapunov theory, this method focuses on adapting CMAC's online learning rules while ensuring stability and convergence. Besides using CMAC, the paper proposes a new signed distance method instead of sliding mode control (SMC) to handle input errors. This method aims to increase flexibility and adaptability and overcome the chattering of SMC in nonlinear systems. In particular, the research also adds a robust controller to ensure stability using Lyapunov to improve the system's accuracy. These recommendations increase the flexibility and accuracy of the control system, helping the system respond more quickly to changes and uncertainties in the operating environment. Finally, to demonstrate the effectiveness of the proposed controller, a five-bar parallel robot was chosen to conduct experiments in case situations. The results show that the proposed controller combined with signed distance achieves higher accuracy than other algorithms and is more stable in all cases mentioned in the research.
Robust Adaptive Iterative Learning Control for De-Icing Robot Manipulator Ngo, Thanh Quyen; Tran, Thanh Hai
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21791

Abstract

This paper introduces a new method of controlling uncertain robot using robust adaptive iterative learning control (RAILC) to track the trajectory in iterative operation mode. This method uses a PD controller combined with gain switching and forward learning techniques to predict the desired torque of the actuator. Using the Lyapunov method, this paper presents an RAILC control scheme for an uncertain robot system with structural and unstructured properties while ensuring the stability of the closed-loop system in the domain repeat. This study believes that this new control method can advance the field of robot control, especially in dealing with structured and unstructured uncertainties. It can help improve the flexibility and performance of robotic systems in real-world applications, such as automated manufacturing, transportation services, or healthcare. At the same time, providing simulation and test results demonstrates the effectiveness of the new control method in deicing high voltage power lines for robots.
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.
Adaptive Task-Space Control of Five-Bar Parallel Robot Dynamic Model with Fully Unknown Using Radial Basis Function Neural Networks for High-Precision Applications Tran, Thanh Hai; Ngo, Thanh Quyen; Uyen, Hoang Thi Tu; Nguyen, Van Tho; Duong, Tien Đoan
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.v6i4.26537

Abstract

Designing a stable and accurate controller for nonlinear systems remains a significant challenge, mainly when the system contains uncertain factors or is affected by external disturbances. This study proposes an adaptive control method based on a Radial Basis Function Neural Network (RBFNN) to effectively estimate the uncertain components in nonlinear systems. The gradient descent algorithm updates the RBFNN parameters, and the control system's stability is rigorously proven based on the Lyapunov theory. The designed controller ensures accuracy under changing conditions and can adapt to nonlinear disturbances and system fluctuations flexibly. Through 45 consecutive test cycles, the system significantly improves precision and outperforms other control methods in comparative tests. This study opens up the potential for broad application in highly uncertain nonlinear MIMO systems, thanks to the effective combination of adaptive learning ability, stability, and simple implementation structure of the proposed controller.
Co-Authors Dao, Viet-Hang Duong, Tien Đoan Hoang, Xuan Hung Lam, Binh Minh Le, Tong Tan Hoa Ngo, Thanh Quyen Nguyen, Phuc-Binh Nguyen, Thanh-Tung Nguyen, Van Sy Nguyen, Van Tho Nguyen, Van-Tuan Than, Phan Minh Uyen, Hoang Thi Tu Vu, Hai