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Journal articles on the topic 'Linear Quadratic Regulator'

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Author: Grafiati

Published: 4 June 2021

Last updated: 3 March 2023

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1

Alexandrova, Mariela, Nasko Atanasov, Ivan Grigorov, and Ivelina Zlateva. "Linear Quadratic Regulator Procedure and Symmetric Root Locus Relationship Analysis." International Journal of Engineering Research and Science 3, no. 11 (November 30, 2017): 27–33. http://dx.doi.org/10.25125/engineering-journal-ijoer-nov-2017-7.

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2

Khlebnikov, M. V., and P. S. Shcherbakov. "Linear Quadratic Regulator: II. Robust Formulations." Automation and Remote Control 80, no. 10 (October 2019): 1847–60. http://dx.doi.org/10.1134/s0005117919100060.

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3

Vissio, Giacomo, Duarte Valério, Giovanni Bracco, Pedro Beirão, Nicola Pozzi, and Giuliana Mattiazzo. "ISWEC linear quadratic regulator oscillating control." Renewable Energy 103 (April 2017): 372–82. http://dx.doi.org/10.1016/j.renene.2016.11.046.

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4

Ochi, Y., and K. Kanai. "Eigenstructure Assignment for Linear Quadratic Regulator." IFAC Proceedings Volumes 29, no. 1 (June 1996): 1098–103. http://dx.doi.org/10.1016/s1474-6670(17)57811-8.

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5

Danas, Aidil, Heru Dibyo Laksono, and Syafii . "Perbaikan Kestabilan Dinamik Sistem Tenaga Listrik Multimesin dengan Metoda Linear Quadratic Regulator." Jurnal Nasional Teknik Elektro 2, no. 2 (September 1, 2013): 72–78. http://dx.doi.org/10.20449/jnte.v2i2.88.

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6

Wu, Guangyu, Lu Xiong, Gang Wang, and Jian Sun. "Linear Quadratic Regulator of Discrete-Time Switched Linear Systems." IEEE Transactions on Circuits and Systems II: Express Briefs 67, no. 12 (December 2020): 3113–17. http://dx.doi.org/10.1109/tcsii.2020.2973302.

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7

NAKAJIMA, Kyohei, Koichi KOBAYASHI, and Yuh YAMASHITA. "Linear Quadratic Regulator with Decentralized Event-Triggering." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E100.A, no. 2 (2017): 414–20. http://dx.doi.org/10.1587/transfun.e100.a.414.

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8

I. Abdulla, Abdulla. "Linear Quadratic Regulator Using Artificial Immunize System." AL-Rafdain Engineering Journal (AREJ) 20, no. 3 (June 28, 2012): 80–91. http://dx.doi.org/10.33899/rengj.2012.50481.

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9

Abdelrahman, M., G. Aryassov, M. Tamre, and I. Penkov. "System Vibration Control Using Linear Quadratic Regulator." International Journal of Applied Mechanics and Engineering 27, no. 3 (August 29, 2022): 1–8. http://dx.doi.org/10.2478/ijame-2022-0031.

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Abstract Balancing a bipedal robot movement against external perturbations is considered a challenging and complex topic. This paper discusses how the vibration caused by external disturbance has been tackled by a Linear Quadratic Regulator, which aims to provide optimal control to the system. A simulation was conducted on MATLAB in order to prove the concept. Results have shown that the linear quadratic regulator was successful in stabilizing the system efficiently.

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10

Gavina, A., J. Matos, and P. B. Vasconcelos. "Tau Method for Linear Quadratic Regulator Problems." Journal of Applied Nonlinear Dynamics 3, no. 2 (June 2014): 139–46. http://dx.doi.org/10.5890/jand.2014.06.004.

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11

Khlebnikov, M. V., P. S. Shcherbakov, and V. N. Chestnov. "Linear-quadratic regulator. I. a new solution." Automation and Remote Control 76, no. 12 (December 2015): 2143–55. http://dx.doi.org/10.1134/s0005117915120048.

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12

Heemels, W. P. M. H., S. J. L. Van Eijndhoven, and A. A. Stoorvogel. "Linear quadratic regulator problem with positive controls." International Journal of Control 70, no. 4 (January 1998): 551–78. http://dx.doi.org/10.1080/002071798222208.

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13

Wu, Guangyu, Jian Sun, and Jie Chen. "Optimal Linear Quadratic Regulator of Switched Systems." IEEE Transactions on Automatic Control 64, no. 7 (July 2019): 2898–904. http://dx.doi.org/10.1109/tac.2018.2872204.

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14

Escárate, Pedro, Juan C. Agüero, Sebastián Zúñiga, Mario Castro, and Javier Garcés. "Linear quadratic regulator for laser beam shaping." Optics and Lasers in Engineering 94 (July 2017): 90–96. http://dx.doi.org/10.1016/j.optlaseng.2017.02.009.

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15

Dibyo Laksono, Heru, and M. Reza Permana. "Analisa Performansi Sistem Kendali Frekuensi Tenaga Listrik Multimesin Dengan Metoda Linear Quadratic Regulator (LQR)." Jurnal Nasional Teknik Elektro 3, no. 2 (September 1, 2014): 167–76. http://dx.doi.org/10.20449/jnte.v3i2.82.

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16

Alonso, C. Amo, D. Ho, and J. M. Maestre. "Distributed Linear Quadratic Regulator Robust to Communication Dropouts." IFAC-PapersOnLine 53, no. 2 (2020): 3072–78. http://dx.doi.org/10.1016/j.ifacol.2020.12.1012.

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17

JOHNSON, C. D. "Limits of propriety for linear-quadratic regulator problems." International Journal of Control 45, no. 5 (May 1987): 1835–46. http://dx.doi.org/10.1080/00207178708933849.

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18

Zhang, Si Qi, Tian Xia Zhang, and Shu Wen Zhou. "Vehicle Dynamics Control Based on Linear Quadratic Regulator." Applied Mechanics and Materials 16-19 (October 2009): 876–80. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.876.

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The paper presents a vehicle dynamics control strategy devoted to prevent vehicles from spinning and drifting out. With vehicle dynamics control system, counter braking are applied at individual wheels as needed to generate an additional yaw moment until steering control and vehicle stability were regained. The Linear Quadratic Regulator (LQR) theory was designed to produce demanded yaw moment according to the error between the measured yaw rate and desired yaw rate. The results indicate the proposed system can significantly improve vehicle stability for active safety.

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19

Bender, D., and A. Laub. "The linear-quadratic optimal regulator for descriptor systems." IEEE Transactions on Automatic Control 32, no. 8 (August 1987): 672–88. http://dx.doi.org/10.1109/tac.1987.1104694.

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20

Grecksch, W., and V. V. Anh. "An Infinite-Dimensional Fractional Linear Quadratic Regulator Problem." Stochastic Analysis and Applications 30, no. 2 (March 2012): 203–19. http://dx.doi.org/10.1080/07362994.2012.649618.

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21

Das, Dibakar, Gurunath Gurrala, and U. Jayachandra Shenoy. "Linear Quadratic Regulator-Based Bumpless Transfer in Microgrids." IEEE Transactions on Smart Grid 9, no. 1 (January 2018): 416–25. http://dx.doi.org/10.1109/tsg.2016.2580159.

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22

Tasch, Uri, and Mark L. Nagurka. "Linear Quadratic Regulator With Varying Finite Time Durations." Journal of Dynamic Systems, Measurement, and Control 114, no. 3 (September 1, 1992): 517–19. http://dx.doi.org/10.1115/1.2897378.

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The optimal state trajectories of time-invariant linear quadratic regulator problems with different time horizons can be found from a single Riccati gain matrix shifted appropriately in time. This result has significant ramifications for real-time implementation of optimal controllers driving systems at various speeds.

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23

Bemporad, Alberto, Manfred Morari, Vivek Dua, and Efstratios N. Pistikopoulos. "The explicit linear quadratic regulator for constrained systems." Automatica 38, no. 1 (January 2002): 3–20. http://dx.doi.org/10.1016/s0005-1098(01)00174-1.

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24

Boukas, E. K., and Z. K. Liu. "Jump linear quadratic regulator with controlled jump rates." IEEE Transactions on Automatic Control 46, no. 2 (2001): 301–5. http://dx.doi.org/10.1109/9.905698.

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25

Possieri, Corrado, Mario Sassano, Sergio Galeani, and Andrew R. Teel. "The linear quadratic regulator for periodic hybrid systems." Automatica 113 (March 2020): 108772. http://dx.doi.org/10.1016/j.automatica.2019.108772.

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26

Cardenas Alzate, Pedro Pablo, German Correa Velez, and Fernando Mesa. "Optimum control using finite time quadratic linear regulator." Contemporary Engineering Sciences 11, no. 95 (2018): 4709–16. http://dx.doi.org/10.12988/ces.2018.89516.

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27

Zhang, D. M., L. Meng, X. G. Wang, and L. L. Ou. "Linear quadratic regulator control of multi-agent systems." Optimal Control Applications and Methods 36, no. 1 (November 11, 2013): 45–59. http://dx.doi.org/10.1002/oca.2100.

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28

Abdul samad, Bdereddin, Mahmoud Mohamed, Fatih Anayi, and Yevgen Melikhov. "An Investigation of Various Controller Designs for Multi-Link Robotic System (Robogymnast)." Knowledge 2, no. 3 (September 6, 2022): 465–86. http://dx.doi.org/10.3390/knowledge2030028.

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An approach to controlling the three-link Robogymnast robotic gymnast and assessing stability is proposed and examined. In the study, a conventionally configured linear quadratic regulator is applied and compared with a fuzzy logic linear quadratic regulator hybrid approach for stabilising the Robogymnast. The Robogymnast is designed to replicate the movement of a human as they hang with both hands holding the high bar and then work to wing up into a handstand, still gripping the bar. The system, therefore has a securely attached link between the hand element and the ‘high bar’, which is mounted on ball bearings and can rotate freely. Moreover, in the study, a mathematical model for the system is linearised, investigating the means of determining the state space in the system by applying Lagrange’s equation. The fuzzy logic linear quadratic regulator controller is used to identify how far the system responses stabilise when it is implemented. This paper investigates factors affecting the control of swing-up in the underactuated three-link Robogymnast. Moreover, a system simulation using MATLAB Simulink is conducted to show the impact of factors including overshoot, rising, and settling time. The principal objective of the study lies in investigating how a linear quadratic regulator or fuzzy logic controller with a linear quadratic regulator (FLQR) can be applied to the Robogymnast, and to assess system behaviour under five scenarios, namely the original value, this value plus or minus ±25%, and plus or minus ±50%. In order to further assess the performance of the controllers used, a comparison is made between the outcomes found here and findings in the recent literature with fuzzy linear quadratic regulator controllers.

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29

Yazici, Hakan, and Mert Sever. "Active control of a non-linear landing gear system having oleo pneumatic shock absorber using robust linear quadratic regulator approach." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 13 (June 14, 2017): 2397–411. http://dx.doi.org/10.1177/0954410017713773.

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This paper deals with the active control of a non-linear active landing gear system equipped with oleo pneumatic shock absorber. Runway induced vibration can cause reduction of pilot’s capability of control the aircraft and results the safety problem before take-off and after landing. Moreover, passenger–crew comfort is adversely affected by vertical vibrations of the fuselage. The active landing gears equipped with oleo pneumatic shock absorber are highly non-linear systems. In this study, uncertain polytopic state space representation is developed by modelling the pneumatic shock absorber dynamics as a mechanical system with non-linear stiffness and damping properties. Then, linear matrix inequalities-based robust linear quadratic regulator controller having pole location constraints is designed, since the classical linear quadratic regulator control design is dealing with linearized state space models without considering the non-linearities and uncertainties. Thereafter, numerical simulation studies are carried out to analyse aircraft response during taxiing. Bump- and random-type runway irregularities are used with various runway class and wide range of longitudinal speed. Simulation results revealed that neglecting the non-linear dynamics associated with oleo pneumatic shock absorber results significant performance degradation. Consequently, it is demonstrated that proposed robust linear quadratic regulator controller has a superior performance in terms of passenger–crew comfort and operational safety when compared to classical linear quadratic regulator.

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30

Tấn, Vũ Văn. "OPTIMAL CONTROLLER DESIGN FOR ACTIVE SUSPENSION SYSTEM ON CARS." TNU Journal of Science and Technology 225, no. 13 (November 30, 2020): 107–13. http://dx.doi.org/10.34238/tnu-jst.3559.

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Hệ thống treo là một trong những bộ phận quan trọng nhất trong thiết kế ô tô và là yếu tố quyết định đến sự thoải mái của lái xe, hành khách (độ êm dịu) và giữ được bám giữa lốp và mặt đường (độ an toàn). Bài báo này giới thiệu một mô hình ¼ ô tô có 2 bậc tự do sử dụng hệ thống treo chủ động với hai bộ điều khiển tối ưu: linear quadratic regulator và linear quadratic gaussian (linear quadratic regulator kết hợp với bộ quan sát Kalman-Bucy). Bằng cách sử dụng bộ quan sát Kalman-Bucy, số lượng cảm biến dùng để đo đạc các tín hiệu đầu vào của bộ điều khiển linear quadratic regulator đã được giảm thiểu tối đa chỉ còn các cảm biến thông thường như gia tốc của khối lượng được treo. Độ êm dịu và an toàn chuyển động khi ô tô sử dụng hệ thống treo chủ động được so sánh với ô tô sử dụng hệ thống treo bị động thông thường thông qua dịch chuyển của khối lượng được treo và gia tốc của nó. Kết quả mô phỏng đã thể hiện rõ giá trị sai lệch bình phương trung bình của gia tốc dịch chuyển thân xe với hệ thống treo tích cực điều khiển tối ưu linear quadratic regulator, linear quadratic gaussian đã giảm khoảng 20% so với ô tô sử dụng hệ thống treo bị động.

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31

Kudinov, Y. I., E. S. Duvanov, I. Y. Kudinov, A. F. Pashchenko, F. F. Pashchenko, G. A. Pikina, A. V. Andryushin, E. K. Arakelyan, and S. V. Mezin. "Construction and Analysis of Adaptive Fuzzy Linear Quadratic Regulator." Journal of Physics: Conference Series 1683 (December 2020): 042065. http://dx.doi.org/10.1088/1742-6596/1683/4/042065.

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32

Ohta, H., M. Kakinuma, and P. N. Nikiforuk. "Use of negative weights in linear quadratic regulator synthesis." Journal of Guidance, Control, and Dynamics 14, no. 4 (July 1991): 791–96. http://dx.doi.org/10.2514/3.20714.

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33

Luo, Jia, and C. Edward Lan. "Determination of weighting matrices of a linear quadratic regulator." Journal of Guidance, Control, and Dynamics 18, no. 6 (November 1995): 1462–63. http://dx.doi.org/10.2514/3.21569.

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34

GERAY, OKAN, and DOUGLAS P. LOOZE. "Linear quadratic regulator loop shaping for high frequency compensation." International Journal of Control 63, no. 6 (April 1996): 1055–68. http://dx.doi.org/10.1080/00207179608921883.

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35

AHMED, N. U., and P. LI. "Quadratic Regulator Theory and Linear Filtering Under System Constraints." IMA Journal of Mathematical Control and Information 8, no. 1 (1991): 93–107. http://dx.doi.org/10.1093/imamci/8.1.93.

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36

Baten, Md Azizul. "Viscosity solution of linear regulator quadratic for degenerate diffusions." Journal of Applied Mathematics and Stochastic Analysis 2006 (May 4, 2006): 1–17. http://dx.doi.org/10.1155/jamsa/2006/48369.

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The paper studied a linear regulator quadratic control problem for degenerate Hamilton-Jacobi-Bellman (HJB) equation. We showed the existence of viscosity properties and established a unique viscosity solution of the degenerate HJB equation associated with this problem by the technique of viscosity solutions.

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37

Gessing, Ryszard. "Discrete-Time Linear-Quadratic Output Regulator for Multivariable Systems." IFAC Proceedings Volumes 34, no. 8 (July 2001): 551–56. http://dx.doi.org/10.1016/s1474-6670(17)40872-x.

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38

Guay, M., R. Dier, J. Hahn, and P. J. McLellan. "Effect of process nonlinearity on linear quadratic regulator performance." Journal of Process Control 15, no. 1 (February 2005): 113–24. http://dx.doi.org/10.1016/j.jprocont.2004.01.009.

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39

Rotea, Mario A., and Jacinto L. Marchetti. "Internal model control using the linear quadratic regulator theory." Industrial & Engineering Chemistry Research 26, no. 3 (March 1987): 577–81. http://dx.doi.org/10.1021/ie00063a026.

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40

Martins, Luís, Carlos Cardeira, and Paulo Oliveira. "Linear Quadratic Regulator for Trajectory Tracking of a Quadrotor." IFAC-PapersOnLine 52, no. 12 (2019): 176–81. http://dx.doi.org/10.1016/j.ifacol.2019.11.195.

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41

Chan, Jenq-Tzong H. "Data-based synthesis of a multivariable linear-quadratic regulator." Automatica 32, no. 3 (March 1996): 403–7. http://dx.doi.org/10.1016/0005-1098(95)00142-5.

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42

Dean, Sarah, Horia Mania, Nikolai Matni, Benjamin Recht, and Stephen Tu. "On the Sample Complexity of the Linear Quadratic Regulator." Foundations of Computational Mathematics 20, no. 4 (August 5, 2019): 633–79. http://dx.doi.org/10.1007/s10208-019-09426-y.

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43

Meghna, P. R., V. Saranya, and B. Jaganatha Pandian. "Design of Linear-Quadratic-Regulator for a CSTR process." IOP Conference Series: Materials Science and Engineering 263 (November 2017): 052013. http://dx.doi.org/10.1088/1757-899x/263/5/052013.

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44

Målqvist, Axel, Anna Persson, and Tony Stillfjord. "Multiscale Differential Riccati Equations for Linear Quadratic Regulator Problems." SIAM Journal on Scientific Computing 40, no. 4 (January 2018): A2406—A2426. http://dx.doi.org/10.1137/17m1134500.

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45

Peng, Zhongxing, and Ying Yang. "Evaluation of Input Redundancies on Linear Quadratic Regulator Problems." Journal of Optimization Theory and Applications 155, no. 1 (April 13, 2012): 325–35. http://dx.doi.org/10.1007/s10957-012-0054-1.

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46

Grieder, Pascal, Francesco Borrelli, Fabio Torrisi, and Manfred Morari. "Computation of the constrained infinite time linear quadratic regulator." Automatica 40, no. 4 (April 2004): 701–8. http://dx.doi.org/10.1016/j.automatica.2003.11.014.

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47

Ali, Rahat, Mohammad Bilal Malik, Fahad Mumtaz Malik, and Muwahida Liaqat. "Error minimizing linear regulation for discrete-time systems." Transactions of the Institute of Measurement and Control 40, no. 4 (January 11, 2017): 1274–80. http://dx.doi.org/10.1177/0142331216681664.

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This paper presents error minimizing linear regulator (EMLR) for discrete-time, time invariant linear systems. The control objective is tracking of reference signal and rejection of disturbances. EMLR design is based on minimization of a quadratic cost function using canonical form of plant model. The proposed control scheme is based on analytical solution applicable for arbitrary initial conditions. In addition, the control design does not require formulation of Linear Matrix Inequalities and Riccati equations, which are otherwise typical requirements of regulators. Stability analysis of the closed loop system using EMLR is presented along with simulation results for regulation of discretized model of underactuated fourth-order ball and beam system.

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48

Wang, Y. J., L. S. Shieh, and J. W. Sunkel. "Linear quadratic regulator approach to the stabilization of matched uncertain linear systems." Journal of Guidance, Control, and Dynamics 14, no. 5 (September 1991): 1074–77. http://dx.doi.org/10.2514/3.20757.

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49

Shauqee, Mohamad Norherman, Parvathy Rajendran, and Nurulasikin Mohd Suhadis. "Proportional Double Derivative Linear Quadratic Regulator Controller Using Improvised Grey Wolf Optimization Technique to Control Quadcopter." Applied Sciences 11, no. 6 (March 17, 2021): 2699. http://dx.doi.org/10.3390/app11062699.

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A hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller is designed for altitude (z) and attitude (roll, pitch, and yaw) control of a quadrotor vehicle. The derivation of a mathematical model of the quadrotor is formulated based on the Newton–Euler approach. An appropriate controller’s parameter must be obtained to obtain a superior control performance. Therefore, we exploit the advantages of the nature-inspired optimization algorithm called Grey Wolf Optimizer (GWO) to search for those optimal values. Hence, an improved version of GWO called IGWO is proposed and used instead of the original one. A comparative study with the conventional controllers, namely proportional derivative (PD), proportional integral derivative (PID), linear quadratic regulator (LQR), proportional linear quadratic regulator (P-LQR), proportional derivative and linear quadratic regulator (PD-LQR), PD2-LQR, and original GWO-based PD2-LQR, was undertaken to show the effectiveness of the proposed approach. An investigation of 20 different quadcopter models using the proposed hybrid controller is presented. Simulation results prove that the IGWO-based PD2-LQR controller can better track the desired reference input with shorter rise time and settling time, lower percentage overshoot, and minimal steady-state error and root mean square error (RMSE).

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50

Yan, Xiao, Zhao-Dong Xu, and Qing-Xuan Shi. "Fuzzy neural network control algorithm for asymmetric building structure with active tuned mass damper." Journal of Vibration and Control 26, no. 21-22 (February 27, 2020): 2037–49. http://dx.doi.org/10.1177/1077546320910003.

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Asymmetric structures experience torsional effects when subjected to seismic excitation. The resulting rotation will further aggravate the damage of the structure. A mathematical model is developed to study the translation and rotation response of the structure during seismic excitation. The motion equations of the structures which cover the translation and rotation are obtained by the theoretical derivations and calculations. Through the simulated computation, the translation and rotation response of the structure with the uncontrolled system, the tuned mass damper control system, and active tuned mass damper control system using linear quadratic regulator algorithm are compared to verify the effectiveness of the proposed active control system. In addition, the linear quadratic regulator and fuzzy neural network algorithm are used to the active tuned mass damper control system as a contrast group to study the response of the structure with different active control method. It can be concluded that the structure response has a significant reduction by using active tuned mass damper control system. Furthermore, it can be also found that fuzzy neural network algorithm can replace the linear quadratic regulator algorithm in an active control system. Because fuzzy neural network algorithm can control the process on an uncertain mathematical model, it has more potential in practical applications than the linear quadratic regulator control method.

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