Lyapunov-global-Lanczos algorithm for model order reduction and adaptive PI controller of large-scale electrical systems

被引:0
|
作者
Kouki, M. [1 ]
Abbes, M. [1 ]
Mami, A. [1 ]
机构
[1] Univ Tunis El Manar, Inst Super Informat & Gest Kairouan, LR Lab Anal Concept & Commande Syst 11 ES20, BP 37, Tunis 1002, Tunisia
关键词
Lanczos; Lyapunov; Model order reduction; Adaptive PI; Global Lanczos; ARDUINO; DYNAMICAL-SYSTEMS; EQUATIONS; ACTUATOR; ARNOLDI; DESIGN;
D O I
10.24200/sci.2017.4368
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Mathematical modeling of complex electrical systems has led us to linear mathematical models of higher order. Consequently, it is difficult to analyze and to design a control strategy for these systems. Order reduction is an important and effective tool to facilitate the handling and designing of a control strategy. In this paper, we firstly present a reduction method, which is based on the Krylov subspace and Lyapunov techniques, called Lyapunov-Global-Lanczos. This method minimizes the H-infinity norm error and absolute error, and preserves the stability of the reduced system. It also provides a better reduced system of order 1, with closer behavior to the original system. This first order system is used to design PI (Proportional-Integral) controller. Secondly, we implement an adaptive digital PI controller in a microcontroller. It calculates the PI parameters in real time, referring to the error between the desired and measured outputs and the initial values of PI controller, that are determined from the first order system. Two simulation examples and a real-time experimentation are presented to show the effectiveness of the proposed algorithms. (C) 2018 Sharif University of Technology. All rights reserved.
引用
收藏
页码:1616 / 1628
页数:13
相关论文
共 50 条
  • [21] Krylov-based controller reduction for large-scale systems
    Gugercin, S
    Antoulas, AC
    Beattie, CA
    Gildin, E
    2004 43RD IEEE CONFERENCE ON DECISION AND CONTROL (CDC), VOLS 1-5, 2004, : 3074 - 3077
  • [22] Efficient model-order reduction application to large-scale linear systems
    Zhou, Wei
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2006, 13E : 3253 - 3258
  • [23] Model reduction of large-scale dynamical systems
    Antoulas, A
    Sorensen, D
    Gallivan, KA
    Van Dooren, P
    Grama, A
    Hoffmann, C
    Sameh, A
    COMPUTATIONAL SCIENCE - ICCS 2004, PT 3, PROCEEDINGS, 2004, 3038 : 740 - 747
  • [24] A New Adaptive Hybrid Algorithm for Large-Scale Global Optimization
    Fan, Ninglei
    Wang, Yuping
    Liu, Junhua
    Cheung, Yiu-ming
    ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT I, 2019, 11554 : 299 - 308
  • [25] Decentralized adaptive controller design of large-scale uncertain robotic systems
    Tan, Kok Kiong
    Huang, Sunan
    Lee, Tong Heng
    AUTOMATICA, 2009, 45 (01) : 161 - 166
  • [26] A Data-Driven Krylov Model Order Reduction for Large-Scale Dynamical Systems
    Hamadi, M. A.
    Jbilou, K.
    Ratnani, A.
    JOURNAL OF SCIENTIFIC COMPUTING, 2023, 95 (01)
  • [27] Efficient Model Order Reduction of Large-Scale Systems on Multi-core Platforms
    Ezzatti, P.
    Quintana-Orti, E. S.
    Remon, A.
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2011, PT V, 2011, 6786 : 643 - 653
  • [28] Uncertainty quantification of large-scale dynamical systems using parametric model order reduction
    Froehlich, Benjamin
    Hose, Dominik
    Dieterich, Oliver
    Hanss, Michael
    Eberhard, Peter
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 171
  • [29] Exhaustive modal analysis of large-scale power systems using model order reduction
    Kouki, M.
    Marinescu, B.
    Xavier, F.
    ELECTRIC POWER SYSTEMS RESEARCH, 2022, 212
  • [30] Projection-Based Model-Order Reduction of Large-Scale Maxwell Systems
    Druskin, V. L.
    Remis, R. F.
    Zaslavsky, M.
    Zimmerling, J. T.
    2017 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), 2017, : 385 - 388