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Algebraic Riccati equation based Q and R matrices selection algorithm for optimal LQR applied to tracking control of 3rd order magnetic levitation system

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents an analytical approach for solving the weighting matrices selection problem of a linear quadratic regulator (LQR) for the trajectory tracking application of a magnetic levitation system. One of the challenging problems in the design of LQR for tracking applications is the choice of Q and R matrices. Conventionally, the weights of a LQR controller are chosen based on a trial and error approach to determine the optimum state feedback controller gains. However, it is often time consuming and tedious to tune the controller gains via a trial and error method. To address this problem, by utilizing the relation between the algebraic Riccati equation (ARE) and the Lagrangian optimization principle, an analytical methodology for selecting the elements of Q and R matrices has been formulated. The novelty of the methodology is the emphasis on the synthesis of time domain design specifications for the formulation of the cost function of LQR, which directly translates the system requirement into a cost function so that the optimal performance can be obtained via a systematic approach. The efficacy of the proposed methodology is tested on the benchmark Quanser magnetic levitation system and a detailed simulation and experimental results are presented. Experimental results prove that the proposed methodology not only provides a systematic way of selecting the weighting matrices but also significantly improves the tracking performance of the system.
Rocznik
Strony
151--168
Opis fizyczny
Bibliogr. 24 poz., fig., tab.
Twórcy
autor
  • 1School of Electrical Engineering, Vellore Institute of Technology Vellore, Tamilnadu, India – 632014
autor
  • 2Department of Instrumentation and Control Engineering, PSG College of Technology Coimbatore, Tamilnadu, India-641004
Bibliografia
  • [1] da Fonseca Neto J.V., Abreu I.S., Nogueira da Silva F. N.eural-Genetic Synthesis for State-Space Controllers Based on Linear Quadratic Regulator Design for Eigenstructure Assignment. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(2): 266-285 (2010).
  • [2] Zaafouri A, Kochbati A, Ksouri M., LQG/LTR control of a direct current motor. Proceedings of the IEEE International Conference on System, Man and Cybernetics, Tunisia 5: 6-9, (2002).
  • [3] Ko H., Lee K.Y., Kim H., An intelligent based LQR controller design to power system stabilization. Electrical Power System Research 71(1): 1-9 (2004).
  • [4] Zhang J.L., Zhang W., LQR self-adjusting based control for the planar double inverted pendulum. Physics Procedia 24 (Part C): 1669-1676 (2012).
  • [5] Balandat M., Zhang W., Abate A., On infinite horizon switched LQR problems with state and control constraints. Systems and Control Letters 61(4): 464-471 (2012).
  • [6] Bevilacqua R., Lehmann T., Romano M., Development and experimentation of LQR/APF guidance and control for autonomous proximity maneuvers of multiple spacecraft. Acta Astronautica 68(7): 1260-1275 (2009).
  • [7] Tao C.W., Taur J.S., Chen Y.C., Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system. Fuzzy Sets and Systems 161(15): 2081-2103 (2010).
  • [8] Ling Wang, Haoqi Ni, Weifeng Zhou et al., MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system. Engineering Applications of Artificial Intelligence 36: 262-268 (2014).
  • [9] Ali N., Miguel A.F., Cristian K., Carlos O.M., Design and implementation of LQR/LQG strategies for oxygen stoichiometry control in PEM fuel cells based systems. Journal of Power Sources 196(9): 4277-4282 (2011).
  • [10] Ang K.K., Wang S.Y., Quek S.T., Weighted energy linear quadratic regulator vibration control of piezoelectric composite plates. Journal of Smart Material Structure 11(1): 98-106 (2002).
  • [11] Hasanzadeh A., Edrington C.S., Liu Y., Leonard J., An LQR based optimal tuning method for IMPbased VSI controller for electric vehicle traction drives. IEEE conference on vehicle power and propulsion conference (VPPC), Chicago 1-7 (2011).
  • [12] Usta M.A., Aircraft roll control system using LQR and fuzzy logic controller. IEEE conference on Innovations in Intelligent Systems and Applications (INISTA), Istanbul, pp. 223-227 (2011).
  • [13] Das S., Pan I., Halder K. et al., LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index. Applied Mathematical Modelling 37(6): 4253-4268 (2013).
  • [14] Tsai S.J., Huo C.L., Yang Y.K, Sun T.Y., Variable feedback gain control design based on particle swarm optimizer for automatic fighter tracking problems. Applied Soft Computing 13(1): 58-75 (2013).
  • [15] Robandia I., Nishimori K., Nishimura R., Ishihara N., Optimal feedback control design using genetic algorithm in multimachine power system. Electrical Power and Energy Systems 23(4): 263-271 (2001).
  • [16] Keshmiri M., Jahromi A.F., Mohebbi A. et al., Modeling and control of ball and beam system using model based and non-model based control approaches. International Journal on Smart Sensing and Intelligent Systems 5(1): 14-35 (2012).
  • [17] Solihin M.I., Akmeliawati R., Particle Swam Optimization for Stabilizing Controller of a Self-erecting Linear Inverted Pendulum. International Journal of Electrical and Electronic Systems Research 3: 410-415 (2010).
  • [18] Vinodh K.E., Jerome J., An Adaptive Particle Swarm Optimization Algorithm for Robust Trajectory Tracking of a Class of Under Actuated System. Archives of Electrical Engineering 63(3): 345-365 (2014).
  • [19] Desineni S.N., Optimal Control Systems. CRC press, (2003).
  • [20] Oral O., Çetin L., Uyar E., A novel method on selection of Q And R matrices in the theory of optimal control. International Journal of Systems Control 1(2): 84-92 (2010).
  • [21] Anderson B.D.O., Moore J.B., Optimal Control: Linear Quadratic Methods. Courier Corporation (2007).
  • [22] Lewis F.L., Vrabie D., Symros V.L., Optimal Control. John Wiley & Sons (2012).
  • [23] AL-Muthairi N.F., Zribi M., Sliding Mode Control of a Magnetic Levitation system. Mathematical Problems in Engineering 93-107 (2004).
  • [24] Quanser Inc., Magnetic Levitation Plant Manual. Canada (2006).
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dfae806e-7313-4c31-89ac-f35a06be26ff
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