Analysis of Control System Models with Conventional LQR and Fuzzy LQR Controller

Kudinov, Y.I.; Olaverri-Monreal, Cristina; Kelina, A.Y.; Vasutin, D.I.; Duvanov, E.S.; Пащенко, А.Ф.

doi:10.1016/j.procs.2019.02.007

Cited by 14 publications

(3 citation statements)

References 2 publications

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“…In 2019, Y. I. Kudinov et al [ 79 ] proposed that the Fuzzy FLC+LQR controller has a much higher speed than the LQR controller, and in 2020, Z. B. Hazem, M. J. Fotuhi, and Z. Bingül [ 80 ] developed F-LQR (Fuzzy LQR) controller and F-LQG (Fuzzy LQG) controller and applied them to the stability control of a two-link rotary inverted pendulum.…”

Section: Discussionmentioning

confidence: 99%

Pre-Work for the Birth of Driver-Less Scraper (LHD) in the Underground Mine: The Path Tracking Control Based on an LQR Controller and Algorithms Comparison

Zhao

et al. 2021

Sensors

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With the depletion of surface resources, mining will develop toward the deep surface in the future, the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient mining of deep space is inseparable from movable and flexible production and transportation equipment such as scrapers. In the new era, intelligence is leading to the development trend of scraper (LHD), path tracking control is the key to the intelligent scraper (LHD), and it is also an urgent problem to be solved for unmanned driving. This paper describes the realization of the automatic operation of articulating the scraper (LHD) from two aspects, a mathematical model and trajectory tracking control method, and it focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, an LQR controller. On this basis, combined with different intelligent clustering algorithms, the parameters of the LQR controller are optimized to find the optimal solution of the LQR controller. Then, the path tracking control of an intelligent LHD unmanned driving technology is studied, focusing on the optimization of linear quadratic optimal control (LQR) and the intelligent cluster algorithms AGA, QPSO, and ACA; this research has great significance for the development of the intelligent scraper (LHD). As mining engineers, we not only need to conduct research for practical engineering projects but also need to produce theoretical designs for advanced mining technology; therefore, the area of intelligent mining is the one we need to explore at present and in the future. Finally, this paper serves as a guide to starting a conversation, and it has implications for the development and the future of underground transportation.

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Section: Discussionmentioning

confidence: 99%

Pre-Work for the Birth of Driver-Less Scraper (LHD) in the Underground Mine: The Path Tracking Control Based on an LQR Controller and Algorithms Comparison

Zhao

et al. 2021

Sensors

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“…The main objective of the LQR controller is to determine the optimal control gains to achieve optimal control of the system [20]. The discrete state-space equation is obtained by applying the midpoint and forward Euler methods to discretize the state and control variables:…”

Section: Lkas Control Strategy 221 Lqr Controllermentioning

confidence: 99%

Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions

Liu,

Hao

et al. 2023

Applied Sciences

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To enhance the control accuracy of lane-keeping assistance systems for trucks encountering crosswind-induced lateral deviations to improve the lateral stability of the vehicle, this study proposes a control strategy based on a linear quadratic regulator (LQR) using a path-tracking preview model. First, the lateral deviation is calculated using the path-tracking preview model. Then, an observer for the vehicle’s sideslip angle is designed using a vehicle lateral tracking deviation model and a Kalman filter controller, and this is used to solve the deviation of the sideslip angle. Finally, a feedforward controller is designed based on the LQR controller and a linear two-degrees-of-freedom vehicle model to eliminate steady-state errors arising from LQR optimization, thereby obtaining the steering angle of the vehicle when subjected to crosswind conditions. Comparing the test results of the sideslip angle, yaw rate, and lateral acceleration demonstrates that this strategy effectively improves the control accuracy of lane-keeping under crosswind conditions. The proposed method is validated through hardware-in-the-loop experiments on a test bench, yielding results consistent with simulations.

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“…The descriptor system found in nature is an example of a fuzzy logic controller test case. In this study a doubleinverted pendulum on a cart, a classical engineering problem [9][10][11], was used to evaluate a fuzzy logic controller (FLC) compared to a method using a linear-quadratic control (LQR), which will be discussed and analyzed [12][13][14]. The purpose of this paper is to strengthen the arguments in favor of an FLC and to demonstrate its potential and the improvements it can offer to existing control methods for complex physical systems in nature that show chaotic behavior.…”

Section: Introductionmentioning

confidence: 99%

Design of a Fuzzy Logic Controller for the Double Pendulum Inverted on a Cart

et al. 2022

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The double-inverted pendulum (DIP) constitutes a classical problem in mechanics, whereas the control methods for stabilizing around the equilibrium positions represent the classic standards of control system theory and various control methods in robotics. For instance, it functions as a typical model for the calculation and stability of walking robots. The present study depicts the controlling of a double-inverted pendulum (DIP) on a cart using a fuzzy logic controller (FLC). A linear-quadratic controller (LQR) was used as a benchmark to assess the effectiveness of our method, and the results showed that the proposed FLC can perform significantly better than the LQR under a variety of initial system conditions. This performance is considered very important when the reduction of the peak system output is concerned. The proposed controller equilibration and velocity tracking performance were explored through simulation, and the results obtained point to the validity of the control method.

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Analysis of Control System Models with Conventional LQR and Fuzzy LQR Controller

Cited by 14 publications

References 2 publications

Pre-Work for the Birth of Driver-Less Scraper (LHD) in the Underground Mine: The Path Tracking Control Based on an LQR Controller and Algorithms Comparison

Pre-Work for the Birth of Driver-Less Scraper (LHD) in the Underground Mine: The Path Tracking Control Based on an LQR Controller and Algorithms Comparison

Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions

Design of a Fuzzy Logic Controller for the Double Pendulum Inverted on a Cart

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