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All Journal Indonesian Journal of Engineering and Science (IJES) Journal of Fuzzy Systems and Control (JFSC)
Le, Thi-Thanh-Hoang
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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 
ANFIS-based LQR Control for Rotary Double Parallel Inverted Pendulum Nguyen, Chi-Hung; Tran, Van-Si; Nguyen, Xuan-Hoang; Truong, Quang-Bao; Nguyen, Minh-Tuan; Luong, Nguyen-Phat; Ngo, Kha-Vy; Nguyen, Duc-Huy; Nguyen, Thanh-Trung; Le, Thi-Thanh-Hoang
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.v2i2.214

Abstract

This article explores two methodologies: Linear Quadratic Regulation (LQR) and the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) on the Rotary Double Inverted Pendulum in Parallel Type (PRDIP) model. This model belongs to a class of underactuated robots, representing a nonlinear system with a mechanically simplistic configuration yet exhibiting considerable nonlinearity. Therefore, ANFIS is utilized to learn the input-output data, responses, and feedback of LQR. The response of the system's output to both LQR and ANFIS is compared to demonstrate the effectiveness of ANFIS in learning from the principles of LQR. This demonstration is supported through three cases: one simulation case and two experimental cases. Both control strategies are applied to the PRDIP system at the zero and -π positions, where one pendulum remains upright, and the other descends to counteract oscillations. The study presents simulation and experimental results to evaluate the points above comprehensively.
Analysis of Linear and Intelligent Control for Balancing Pendubot System Tran, Minh-Duy; Le, Diep-Thuy-Duong; Phan, Hong-Phuoc; Vo, Hoang-Viet; Ngo, Dang-Quang-Tinh; Nguyen, Ngoc-Duy; Nguyen, Tan-Phat; Tran, Nhat-Linh; Vo, Thanh-An; Le, Thi-Thanh-Hoang
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.v3i1.272

Abstract

Pendubot is a typical under-actuated SIMO control system, commonly used in research on control algorithms. Rather than focusing on analyzing a single control algorithm, this paper provides an overview of control efficiency as well as differences between algorithms through analytical assessments. In this study, the authors analyzed algorithms including feedback linearization (a linear algorithm), LQR – optimal control (a linear algorithm), and fuzzy control (an intelligent algorithm) to stabilize the model at the equilibrium position of the TOP position – where both bars of the system stand upright in the opposite direction to gravity. The genetic algorithm (GA) is used to optimize control parameters for the model. These algorithms are simulated in MATLAB/Simulink, and the simulation results are compared, concluding that the LQR control algorithm is the most optimal for balancing this model.
A Study of Optimized-LQR Control for Rotary Inverted Pendulum by Particle Swarm Optimization Le, Thanh-Tri-Dai; Pham, Thanh-Cong; Bui, Duc-Thanh-Long; Nguyen, Quang-Truong; Vo, Van-Nhat-Truong; Dinh, Quoc-Lap; Tran, Le-Hieu; Truong, Thien-Bao; Nguyen, Tan-Loc; Nguyen, Duy-Tan; Nguyen, Tuan-Anh; Nguyen, Viet-Anh; Le, Thi-Thanh-Hoang
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.v3i2.301

Abstract

Rotary Inverted Pendulum (RIP) is a classical but effective model in testing control algorithms. Besides designing controllers, it can also be a model for testing the evolution algorithms (EAs) in optimizing control parameters. In this paper, we apply particle swarm optimization (PSO), which is an EA, to optimize the parameters of the LQR controller for this model. In the study, an experimental model in which system parameters are already measured and identified in former studies is used. The LQR control method is inherited from former results, and the weighing matrices (Q and R) are optimized by the PSO method. In each case, the control matrix K is obtained from Q and R to apply for RIP. Through both simulation and experiment, LQR control parameters are found better through generations by using PSO. The responses of RIP, in which controllers are designed under optimized Q and R in later generations, are better in quality, and values of the fitness function also supports that opinion. Thence, through this study, beside genetic algorithm (GA), this study proves that PSO is a suitable searching algorithm that can be applied for balancing this single input- multi output (SIMO) system. Also, the experimental platform of RIP in this research confirms its ability to control tests.
Co-Authors Bui, Duc-Thanh-Long Dao, Anh-Quan Dinh, Quoc-Lap Hoang, Ngoc-Huy Le, Diep-Thuy-Duong Le, Manh-Tin Le, Phuong-Tien Le, Thanh-Tri-Dai Le, Van-Duc-Thinh Luong, Nguyen-Phat Ngo, Dang-Quang-Tinh Ngo, Kha-Vy Nguyen, Ba-Thanh Nguyen, Chi-Hung Nguyen, Duc-Huy Nguyen, Duy-Tan Nguyen, Hai-Dang Nguyen, Minh-Tuan Nguyen, Ngoc-Duy Nguyen, Quang-Truong Nguyen, Tan-Loc Nguyen, Tan-Phat Nguyen, Thanh-Cong Nguyen, Thanh-Trung Nguyen, Tuan-Anh Nguyen, Tuan-Loc Nguyen, Tuan-Vu Nguyen, Viet-Anh Nguyen, Xuan-Hoang Pham, Thanh-Cong Phan, Hong-Phuoc Phan, Ngoc-Tho Tran, Le-Hieu Tran, Minh-Duy Tran, Nhat-Linh Tran, Van-Si Truong, Quang-Bao Truong, Thien-Bao Vo, Hoang-Trong-Thai Vo, Hoang-Viet Vo, Thanh-An Vo, Van-Nhat-Truong