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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

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

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.

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

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.

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

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.

Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery Nguyen, Trung Dung; Pham, Trung Kien; Ha, Chi Kien; Le, Long Ho; Ngo, Thanh Quyen; Nguyen, Hoanh
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.6742

Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multi-layer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy.

Integration of Modbus-Ethernet Communication for Monitoring Electrical Power Consumption, Temperature, and Humidity Le, Long Ho; Ngo, Thanh Quyen; Toan, Nguyen Duc; Nguyen, Chi Cuong; Phong, Bui Hong
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.v5i6.22456

Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, temperature, and humidity. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. Based on the requirement, we proposed a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by FINECO, the KLEA 220P three-phase multifunction meter by KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are involved. Finally, the collected data of electrical consumption, temperature, and humidity can be stored on an SD card, transmitted to the cloud for real-time monitoring on mobile devices, and facilitated by the ESP-WROOM-32 microcontroller from Espressif system.

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

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.

Integration of Modbus-Ethernet Communication for Monitoring Electrical Power Consumption, Temperature, and Humidity Le, Long Ho; Ngo, Thanh Quyen; Toan, Nguyen Duc; Nguyen, Chi Cuong; Phong, Bui Hong
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.v5i6.22456

Effective management of electrical energy requires monitoring, controlling, and storing parameters gathered from power measurement devices including voltage, current, temperature, and humidity. This assessment of the quality of electrical energy is essential for management organizations, power companies, and individual consumers to develop efficient electricity usage plans. Based on the requirement, we proposed a hardware implementation for data collection and online communication software integrated with a system for collecting data on consumption of electrical energy. The EM115-Mod CT multifunction industrial meters by FINECO, the KLEA 220P three-phase multifunction meter by KLEMSAN, and the ME96SS–ver.B by MITSUBISHI are involved. Finally, the collected data of electrical consumption, temperature, and humidity can be stored on an SD card, transmitted to the cloud for real-time monitoring on mobile devices, and facilitated by the ESP-WROOM-32 microcontroller from Espressif system.

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

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.

Real-Time Experimental Study of Speed Control for PMSM Drive System on OPAL-RT Simulator Using Radial Basis Function Neural Network Hoang, Xuan Hung; Tran, Thanh Hai; Than, Phan Minh; Ngo, Thanh Quyen; Nguyen, Van Sy; Le, Tong Tan Hoa
International Journal of Robotics and Control Systems Vol 5, No 3 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i3.2048

This paper addresses the problem of improving speed control accuracy and disturbance rejection capability for Permanent Magnet Synchronous Motors (PMSMs), which are widely used in industrial applications requiring high-performance drives. Conventional controllers such as PID often exhibit limited performance under nonlinear and time-varying conditions. The sliding mode control combined with a Radial Basis Function Neural Network (RBFNN) is proposed to enhance robustness and adaptability to overcome these limitations. The main contribution of this study is the integration of an adaptive RBFNN to estimate and compensate for unknown disturbances in real time, ensuring precise and stable motor operation. The theoretical stability of the system is guaranteed based on Lyapunov’s theory. The proposed method is implemented in a MATLAB/Simulink environment and tested on the OPAL-RT OP5707XG real-time hardware platform. The control system includes a speed loop using the RBFNN and a current loop for field-oriented control. The motor is subjected to varying speed commands in three stages to evaluate performance under dynamic conditions. Simulation results show that the RBFNN controller significantly improves speed tracking accuracy, reduces overshoot, and adapts better to sudden changes compared to conventional PID control. Real-time experimental results further confirm the effectiveness of the controller, despite the presence of noise and hardware delays. Current control performance also demonstrates better torque production and phase symmetry under dynamic loading with the RBFNN. A comparative analysis between simulation and experimental data highlights the practical applicability of the proposed approach.

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