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All Journal Journal of Fuzzy Systems and Control (JFSC)
Nguyen, Hoang-Son
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Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Modeling and Optimal Control for Two-Wheeled Self-Balancing Robot Do, Quoc-Thinh; Tran, Van-Thanh; Ngo, Minh-Thai; Tran, Minh-Quan; Thiem, Quan-Linh; Nguyen, Hoang-Son; Pham, Ba-Khoi; Phan, Nguyen-Phuoc-An; Nguyen, Duy-Hieu; Nguyen, Duc-Hoc; Nguyen, Van-Hoc; Tran, Ho-Minh-Quang; Le, ThiHongLam
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.v2i1.162

Abstract

The two-wheeled self-balancing robot based on an inverted pendulum model is a nonlinear object with uncertain parameters that are difficult to control with 6 state variables. This is a multiple input-multiple output (MIMO) under-actuated system that is very complex and causes many challenges for the operator. This paper analyzed the mathematical equation of a two-wheeled self-balancing robot vehicle system. Then, the Linear Quadratic Regulator (LQR) control is applied to the system through simulation on Matlab/Simulink and experiment. The results show that the LQR algorithm has been successfully applied in many moving cases.
A Study of Adaptive Model Predictive Control for Rotary Inverted Pendulum Huynh, Phuc-Hoang; Le, Khac-Chan-Nguyen; Nguyen, Truong-Phuc; Tran, Hoang-Dang-Khoa; Dang, Su-Truong; Nguyen, Thanh-Quyen; Le, Thang-Phong; Nguyen, Huu-Hanh; Tran, Pham-Hong-Linh; Nguyen, Hau-Phuong; Nguyen, Hoang-Son; Nguyen, Tai-Truong; Nguyen, Hai-Thanh
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.v3i2.302

Abstract

This paper proposes an Adaptive Model Predictive Control (MPC) approach for the rotary inverted pendulum (RIP). The method combines Linear Time-Varying (LTV) models at each sampling instant with a Linear Time-Varying Kalman Filter (LTVKF) for state estimation. By predicting and adapting to dynamic system changes, the controller achieves trajectory tracking performance comparable to non-adaptive MPC. However, the Adaptive MPC extends the arm’s operating range by up to 1.5 times, making it a promising solution for strongly nonlinear or time-varying systems like the RIP.
Co-Authors Dang, Su-Truong Do, Quoc-Thinh Huynh, Phuc-Hoang Le, Khac-Chan-Nguyen Le, Thang-Phong Le, ThiHongLam Ngo, Minh-Thai Nguyen, Duc-Hoc Nguyen, Duy-Hieu Nguyen, Hai-Thanh Nguyen, Hau-Phuong Nguyen, Huu-Hanh Nguyen, Tai-Truong Nguyen, Thanh-Quyen Nguyen, Truong-Phuc Nguyen, Van-Hoc Pham, Ba-Khoi Phan, Nguyen-Phuoc-An Thiem, Quan-Linh Tran, Ho-Minh-Quang Tran, Hoang-Dang-Khoa Tran, Minh-Quan Tran, Pham-Hong-Linh Tran, Van-Thanh