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Nguyen, Van-Dong-Hai
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Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Implementation of Fuzzy-PID Controller for 2-DOF Helicopter Huynh, Thanh-Do; Bach, Ngoc-Thanh; Le, Quang-Dao; Le, Duong-Dong; Bui, Viet-Hoang; Vo, Hoai-An; Nguyen, Viet-Nhat-Long; Pham, Phi-Hung; Nguyen, Huu-Dat; Nguyen, Van-Dong-Hai
Journal of Fuzzy Systems and Control Vol. 2 No. 2 (2024): Vol. 2, No. 2, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i2.204

Abstract

This article presents the model and control of a 2-DOF helicopter, a multi-input multi-output (MIMO) system that is set up in the laboratory. The conventional mathematical model, employing the Euler-Lagrange method, is utilized in this study to conduct the system modeling process. The transfer functions derived from this model are then incorporated into diverse control methodologies to optimize PID gain coefficients. The PID controllers are employed to control this system. In addition, we use a Fuzzy controller to adjust the Kp, Ki, and Kd coefficients of this PID. As a result, we obtain a fuzzy PID controller with superior control quality than a PID controller. Under Fuzzy-PID, the system operates more stably, overcoming some weaknesses of the PID linear controller. The state space model is built by considering specific design assumptions and simplifications. Results are obtained through simulation and testing on the model.
LQR Control for Experimental Double Rotary Inverted Pendulum Tran, Nhat-Cuong; Nguyen, Van-Dong-Hai; Le, Chi-Thanh; Lai, Anh-Hai; Nguyen, Trong-Phung; Huynh, Minh-Tuan; Phan, Viet-Thanh; Tong, Gia-Dat; Nguyen, Le-Thanh-Dat; Ngo, Trinh-Anh-Tuan
Journal of Fuzzy Systems and Control Vol. 2 No. 2 (2024): Vol. 2, No. 2, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i2.212

Abstract

LQR control and rotary inverted pendulum (RIP) are already a classic and typical category in the field of automatic control algorithms. From the experience of a 1-order system, we study and apply the LQR algorithm to the 2-step system (DRIP – Double Rotary Inverted Pendulum). In this study, the authors will present kinematic equations of the DRIP system, the method of building an LQR controller for the system in the balance position of bar 1 upwards bar 2 downwards (the first pendulum balances at 0, the second pendulum balances at 180) and build a practical model to investigate the stability of the system. Our method is proven to balance one link well and anti-fluctuation another link well for this model through both simulation and experiment.
Model Predictive Control for Rotary Inverted Pendulum: Simulation and Experiment Huynh, Phuc-Hoang; Nguyen, Minh-Hanh; Pham, Nguyen-Phat; Duong, Hoang-Viet-Phuc; Nguyen, Huy-Ha; Le, Duc-Chung; Nguyen, Minh-Khoa; Bui, Ngoc-Liem; Le, Nguyen-Phi-Long; Nguyen, Van-Dong-Hai
Journal of Fuzzy Systems and Control Vol. 2 No. 3 (2024): Vol. 2, No. 3, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i3.263

Abstract

Rotary Inverted Pendulum (RIP) is one of the simplest nonlinear systems commonly used for validating control algorithms. In this study, two controllers, Model Predictive Control (MPC) and Linear Quadratic Regulation (LQR), are simulated and experimentally validated. These controllers are executed in real-time on a PC, while the STM32F407 chip handles control and data acquisition from the pendulum using a high-speed USB interface. Due to the custom-built nature of this model, there are inaccuracies in the model and parameter identification. However, results show that the MPC controller is better at trajectory tracking and maintaining balance near the set point compared to the LQR controller. On the other hand, the LQR controller responds more robustly to disturbances and external forces, highlighting distinct differences between MPC’s optimization over each prediction horizon and LQR’s single-solution approach for the entire prediction horizon.
Backstepping Control for Ball and Beam: Simulation and Experiment Tran, Vo-Hoang-Lap; Le, Trung-Hieu; Hoang, Dai-Phuc; Nguyen, Van-Dong-Hai; Ho, Ngoc-Thinh; Do, Tien-Phat; Le, Tuan-Cuong; Tran, Thi-Xuan-Hy; Luong, The-Duy; Vo, Thanh-Son; Nguyen, Phuoc-Khanh; Nguyen, Minh-Tam
Journal of Fuzzy Systems and Control Vol. 3 No. 1 (2025): Vol. 3, No. 1, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i1.275

Abstract

This paper presents the modeling and control of the Ball and Beam system, a commonly used Single Input – Multiple Output (SIMO) system in control research experiments. In the study, the Backstepping method is applied to model and control the system. The linear differential equations describing the system's dynamics are derived based on fundamental mechanical principles, using the Euler-Lagrange method to develop an accurate mathematical model. Subsequently, the backstepping method is employed to design a controller that ensures the global stability of the system. Lyapunov theory is applied to prove the system's stability, with an appropriate Lyapunov function selected to guarantee the global stability of the controller. In addition to simulations, the study also conducts experiments to test the system's stability under Backstepping control. The results show that this controller is not only effective in maintaining balance and controlling the position of the ball on the beam but also addresses the limitations of traditional linear control methods. Both simulation and experimental results demonstrate the high performance and stability of the system, confirming the stability according to Lyapunov theory.
Trajectories Tracking Control for Rotary Inverted Pendulum using Backstepping Method Pham, Ha-Gia-Bao; Nguyen, Huy-Khai; Nguyen, Tran-Quoc-Tuan; Nguyen, Van-Dong-Hai; Dao, Ngoc-Quy; Ngo , Van-Quy-Hai; Tran, Thanh-Son; Phan, Hien-Dat; Chu, Gia-Huy; Huynh, Hoang-Tien-Phat
Journal of Fuzzy Systems and Control Vol. 3 No. 1 (2025): Vol. 3, No. 1, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i1.276

Abstract

Rotary inverted pendulum (RIP) is a fundamental yet challenging benchmark system in control engineering due to its nonlinear dynamics, instability, and underactuated nature. This study addresses the problem of trajectory tracking control for RIP, which is critical for ensuring system stability and accurate motion control in various engineering applications. Simulation results demonstrate that the backstepping approach achieves superior performance in terms of tracking accuracy, robustness, and convergence speed compared to traditional methods. The findings emphasize the effectiveness of backstepping in addressing control challenges in nonlinear systems, offering insights for future research in both theoretical advancements and real-world applications.
Fuzzy Controller from Experts’ Rules for Middle Axis Ball and Beam Nguyen, Minh-Quan; Nguyen, Manh-Cuong; Trinh, Quang-Huy; Nguyen, Trung-Nghia; Ngo, Van-Thiet; Phu, Huynh-Manh-Trien; Nguyen, Pham-Minh-Duc; Nguyen, Phu-Tan; Le, Van-Truong; Dinh, Le-Hai-Duong; Dam, Thuan-An; Nguyen, Duc-Huy; Nguyen, Van-Dong-Hai
Journal of Fuzzy Systems and Control Vol. 1 No. 3 (2023): Vol. 1, No. 3, 2023
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v1i3.94

Abstract

The present study examined the nonlinear system of Ball and Beam, to design an intelligent controller for this dynamic system. The authors formulated a mathematical model for the system and performed simulation testing using MATLAB to control it. It is noteworthy that the mathematical model was exclusively used for simulation purposes and not for building the controller. The author's team then proceeded to develop a Fuzzy Controller for the simulation model of the Ball and Beam system. Subsequently, the team tested the Fuzzy controller on the actual Ball and Beam model. The primary objective of this study was to assess the feasibility of building and controlling an intelligent controller for a nonlinear object, without relying on its mathematical model. The findings of the study can be useful in designing and controlling complex systems that are difficult to model mathematically.
PID-LQR Combined Linear Controller for Balancing Ballbot: Simulation and Experiment Nguyen, Van-Dong-Hai; Cu, Minh-Phuoc; Nguyen, Tran-Minh-Nguyet; Huynh, Thanh-Do; Dang, Dinh-Khoi; Hoang, Tan-Dat; Nguyen, Minh-Quan; Vu, Dinh-Dung; Le, Chi-Hai-Duong; Phan, Nguyen-Bao-Long; Bui, Quoc-Duy; Le, Ngoc-Hai; Vo, Duy-Phuc
Journal of Fuzzy Systems and Control Vol. 1 No. 3 (2023): Vol. 1, No. 3, 2023
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v1i3.153

Abstract

Ballbot is a robotic structure in which the robot self-balances on a ball by rotating wheels. This robot is a popular form of service robot. Developing controllers for this system provides academic tools for reality. In this paper, after presenting the dynamic equations of the ballbot, we design a Proportional Integrated Derivative (PID)-Linear Quadratic Regulator (LQR) combined (PID-LQR) controller to balance the robot on the ball. The simulation results show the success of this method. An experimental model of a ballbot is presented. In the experiment, PID-LQR combined controller also shows its ability to self-balancing for the ballbot. With this finding, a method of controlling this model is a reference for developing this service robot.
Position Control of 3-DOF Experimental Articulated Robot Arm using PID Controller Vo, Dinh-Hieu; Le, Nam-Chau; Nguyen, Thi-Y-Nhi; Huynh, Tran-Phuong; Huynh, Nhat-Truong; Tran, Kim-Huy; Nguyen, Ba-Chinh; Do, Truong-Giang; Tran, Gia-Huy; Nguyen, Van-Dong-Hai
Journal of Fuzzy Systems and Control Vol. 3 No. 1 (2025): Vol. 3, No. 1, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i1.291

Abstract

In this paper, we are simulating a 3-degrees of freedom (DOF) Articulated Robot Arm, calculating kinematics and building a PID controller for the 3-Dof Articulated Robot Arm. First, the design process of the 3-Dof robot arm model is done on the Solid works platform. Second, the PID control method is used to determine the "error" value which is the between the measured value of the variable parameter and the desired set value. The controller will minimize errors by adjusting the input control value. Finally, a real robot arm was controlled to move following the reference in the plane with the PID controller embedded on the Arduino microcontroller and collected data about the computer.
LQR Controller Based on BAT Algorithm for Rotary Double Parallel Inverted Pendulum Le, Thanh-Tri-Dai; Nguyen, Ngoc-Kien; Le, Phuc-Truong; Bui, Minh-Nguyen-Bao; Nguyen, Trong-Tin; Tran, Chi-Anh; Doan, Phuong-Tu; Dao, Duc-Nhan; Nguyen, Van-Dong-Hai; Nguyen, Thanh-Tung
Journal of Fuzzy Systems and Control Vol. 3 No. 2 (2025): Vol. 3, No. 2, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i2.304

Abstract

This paper presents an enhanced approach to stabilizing the Rotary Double Parallel Inverted Pendulum (RDPIP) through a combination of the LQR method and the BAT algorithm. Traditionally, selecting appropriate Q and R matrices relies on designers' intuitions or trial-and-error processes, often resulting in suboptimal performance. By leveraging the BAT algorithm’s swarm intelligence, the proposed method automatically optimizes the cost function to yield improved control performance. Key improvements include shorter stabilization time, reduced overshoot, and minimized oscillations. Simulation results show that the BAT-enhanced LQR controller significantly outperforms traditional design in terms of convergence speed and system damping. These findings underscore the potential of metaheuristic algorithms in refining classical control strategies for complex, nonlinear systems.
An LQR-Based ANFIS Control for Double-Linked Inverted Pendulum on Cart Pham, Truong-Phuong-Nam; Tran, Trong-Bang; Nguyen, Van-Dong-Hai; Nguyen, Tai-Tue; Nguyen, Gia-Thinh; Nguyen, Duy-Phat; Nguyen, Dong-Khang; Ha, Van-An; Trinh, The-Nam-Chau; Nguyen, Trung-Thang
Journal of Fuzzy Systems and Control Vol. 3 No. 2 (2025): Vol. 3, No. 2, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i2.307

Abstract

This paper presents a double-linked inverted pendulum on a cart system, which is highly nonlinear and inherently unstable. In the simulation, the state variable outputs are processed through three ENCODER blocks with a resolution of 1000 pulses, as we aim to develop a mathematical model that closely approximates real-world experiments. The objective of this study is to use an ANFIS controller to learn from data that closely resembles the actual system behavior under an LQR controller and apply it in a simulation environment to evaluate the stability and response of the system under both ANFIS and LQR controllers. The results show that the ANFIS controller provides better responses than the LQR controller.
Co-Authors Bach, Ngoc-Thanh Bui, Minh-Nguyen-Bao Bui, Ngoc-Liem Bui, Quoc-Duy Bui, Viet-Hoang Chu, Gia-Huy Cu, Minh-Phuoc Dam, Thuan-An Dang, Dinh-Khoi Dao, Duc-Nhan Dao, Ngoc-Quy Dao, Viet-Thinh Dinh, Le-Hai-Duong Do, Thi-Thuy-Duong Do, Tien-Phat Do, Truong-Giang Doan, Nam-Long Doan, Phuong-Tu Duong, Hoang-Viet-Phuc Ha, Van-An Ho, Ho-Anh-Vu Ho, Ngoc-Thinh Ho, Quang-Thuan Hoang, Anh-Son Hoang, Dai-Phuc Hoang, Tan-Dat Hoang, Thi-Quynh-Huong Huynh, Hoang-Tien-Phat Huynh, Minh-Tuan Huynh, Nhat-Truong Huynh, Phuc-Hoang Huynh, Thanh-Do Huynh, Tran-Phuong Lai, Anh-Hai Le, Chi-Hai-Duong Le, Chi-Thanh Le, Duc-Chung Le, Duong-Dong Le, Duy-Thinh Le, Nam-Chau Le, Ngoc-Hai Le, Nguyen-Phi-Long Le, Phuc-Truong Le, Quang-Dao Le, Thanh-Tri-Dai Le, Trung-Hieu Le, Tuan-Cuong Le, Van-Truong Luong, Pham-Kien-Quoc Luong, The-Duy Ngo , Van-Quy-Hai Ngo, Trinh-Anh-Tuan Ngo, Van-Thiet Nguyen, Anh-Duc Nguyen, Ba-Chinh Nguyen, Binh-Hau Nguyen, Binh-Hau Nguyen Nguyen, Dong-Khang Nguyen, Duc-Hieu Nguyen, Duc-Huy Nguyen, Duy-Phat Nguyen, Gia-Thinh Nguyen, Hoang-Ha Nguyen, Huu-Dat Nguyen, Huy-Ha Nguyen, Huy-Khai Nguyen, Khanh-Dang Nguyen, Le-Cong-Vinh Nguyen, Le-Phuc-Khuong Nguyen, Le-Thanh-Dat Nguyen, Manh-Cuong Nguyen, Minh-Hanh Nguyen, Minh-Khoa Nguyen, Minh-Quan Nguyen, Minh-Tam Nguyen, Ngoc-Kien Nguyen, Pham-Minh-Duc Nguyen, Phu-Tan Nguyen, Phuoc-Khanh Nguyen, Phuong-Quang Nguyen, Tai-Tue Nguyen, Thai-Duong Nguyen, Thanh-Tung Nguyen, Thi-Y-Nhi Nguyen, Tran-Minh-Nguyet Nguyen, Tran-Quoc-Tuan Nguyen, Tran-Thanh-Thuy Nguyen, Trong-Phung Nguyen, Trong-Tin Nguyen, Trung-Nghia Nguyen, Trung-Thang Nguyen, Viet-Nhat-Long Nguyen, Viet-Truong Pham, Ha-Gia-Bao Pham, Nguyen-Phat Pham, Phi-Hung Pham, Truong-Phuong-Nam Phan, Hien-Dat Phan, Nguyen-Bao-Long Phan, Viet-Thanh Phu, Huynh-Manh-Trien Tong, Gia-Dat Tran, Chi-Anh Tran, Gia-Huy Tran, Kim-Huy Tran, Nhat-Cuong Tran, Tan-Tai Tran, Thanh-Son Tran, Thi-Xuan-Hy Tran, Trong-Bang Tran, Vo-Hoang-Lap Trinh, Quang-Huy Trinh, The-Nam-Chau Trung, Mai-Bao Vo, Dinh-Hieu Vo, Duy-Phuc Vo, Hoai-An Vo, Thanh-Son Vo, Viet-Khoi Vu, Dinh-Dung