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All Journal Indonesian Journal of Engineering and Science (IJES) Journal of Fuzzy Systems and Control (JFSC)
Tran, Trong-Bang
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Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Fuzzy Systems and Control (JFSC)

 1 
Trajectory Tracking using LQR Control for Pendubot: Simulation and Experiment Tran, Trong-Bang; Nguyen, Hoang-Thien; Nguyen, Tay; Dang, Duc-Dat; Pham, Duong-Minh-Quang; Le, Nhat-Duy; Huynh, Hoang-Khuong; Phan, Thanh-Quoc-Du; Nguyen, Bao-Huy; Nguyen, Ngo-Huu-Tung; Pham, Le-Quoc-Toan; Nguyen, Trung-Hieu; Dang, Quang-Vinh
Journal of Fuzzy Systems and Control Vol. 2 No. 1 (2024): Vol. 2, No. 1, 2024
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v2i1.163

Abstract

Pendubot, a unique single-input-multiple-output (SIMO) system, is commonly employed in laboratories to validate control algorithms. In this article, we develop an LQR controller to simulate and assess its effectiveness on this model. Specifically targeting the TOP position for control, we not only verify the controller's quality but also ensure the motion system accurately tracks a predefined trajectory, encompassing sine and square pulses. Control parameters are meticulously chosen through a genetic algorithm (GA). Although LQR is not highly rated for trajectory tracking due to its relatively small operational range, our successful simulations and control of this system are attributed to the assistance of GA
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.
Intelligent Control for 2D-Crane System Huynh, Trung-Son; Dinh, Dang-Khoa; Tran, Trong-Bang; Dang, Huu-Loc; Le, Dinh-Nguyen-Phuc; Bui, Hung-Thinh; Le, Hoang-Lam; Nguyen, Thanh-Binh; Nguyen, Van-Hiep; Nguyen, Le-Nhat-Minh; Dang, Thien-Quoc; Nguyen, Ngoc-Hung; Nguyen, Thi-Ngoc-Thao; Pham, Huynh-Duc; Nguyen, Xuan-Tien; Nguyen, Van-Dong-Hai
Journal of Fuzzy Systems and Control Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i1.350

Abstract

This paper presents an Intelligent Learning-based Control approach for a 2D Crane System, aiming to evaluate the learning capability of various intelligent techniques based on a baseline Fuzzy Logic Controller (FLC). The initial fuzzy controller is designed for position and sway control, while Genetic Algorithm (GA), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed in simulation to retrain and enhance its performance. Comparative results show that intelligent learning methods can significantly improve system response, reduce overshoot, and increase robustness compared to the original fuzzy controller. Moreover, an experimental setup using the baseline FLC is implemented to verify the practical effectiveness of the fuzzy control approach on a real 2D crane system. The findings highlight the potential of intelligent learning techniques for future real-time implementation.
Co-Authors Bui, Hung-Thinh Dang, Duc-Dat Dang, Huu-Loc Dang, Quang-Vinh Dang, Thien-Quoc Dinh, Dang-Khoa Do, Ngoc-Huy Ha, Van-An Huynh, Hoang-Khuong Huynh, Trung-Son Le, Dinh-Nguyen-Phuc Le, Hoang-Lam Le, Nhat-Duy Le, Thi-Hong-Lam Lieu, Vinh-Hung Nguyen, Bao-Huy Nguyen, Binh-Hau Nguyen, Dong-Khang Nguyen, Duy-Phat Nguyen, Gia-Thinh Nguyen, Hoang-Thien Nguyen, Le-Nhat-Minh Nguyen, Minh-Khoa Nguyen, Ngo-Huu-Tung Nguyen, Ngoc-Hung Nguyen, Tai-Tue Nguyen, Tay Nguyen, Thanh-Binh Nguyen, Thi-Ngoc-Thao Nguyen, Trung-Hieu Nguyen, Trung-Thang Nguyen, Van-Dong-Hai Nguyen, Van-Hiep Nguyen, Xuan-Tien Pham, Duong-Minh-Quang Pham, Huynh-Duc Pham, Le-Quoc-Toan Pham, Truong-Phuong-Nam Phan, Thanh-Quoc-Du Trinh, The-Nam-Chau