Data-driven controller tuning using frequency domain specifications

被引:17
|
作者
Garcia, Daniel [1 ]
Karimi, Alireza [1 ]
Longchamp, Roland [1 ]
机构
[1] Ecole Polytech Fed Lausanne, Lab Automat, Stn 9, CH-1015 Lausanne, Switzerland
关键词
D O I
10.1021/ie0513043
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
This paper presents an overview of our recent work on a model-free proportional- integral - derivative (PID) controller tuning procedure. The method can handle different stability and performance indicators in the frequency domain. The phase margin, gain margin, crossover frequency, and more-advanced indicators, which are the infinity-norm of the sensitivity functions, can be considered for the design. The actual values of the design parameters are measured directly on the system, thanks to closed-loop experiments. A frequency criterion is then defined as the weighted sum of squared errors between the measured and desired values of the design parameters. The minimization is done iteratively using the Gauss-Newton algorithm. The approach presented does not require any parametric model of the plant and can be applied to a wide range of industrial applications. Simulation examples show the rapid convergence of the algorithm and the effectiveness of the method for PID controller tuning.
引用
收藏
页码:4032 / 4042
页数:11
相关论文
共 50 条
  • [31] Data-Driven Robust Servo Tuning Method Using Fractional-Order PID Controller
    Jinai, K.
    Kawaguchi, N.
    Arrieta, O.
    Sato, T.
    IFAC PAPERSONLINE, 2024, 58 (07): : 436 - 441
  • [32] Frequency-Domain Data-Driven Predictive Control
    Meijer, T. J.
    Nouwens, S. A. N.
    Scheres, K. J. A.
    Dolk, V. S.
    Heemels, W. P. M. H.
    IFAC PAPERSONLINE, 2024, 58 (18): : 86 - 91
  • [33] Data-driven modeling of multiaxial fatigue in frequency domain
    Ravi, Sandipp Krishnan
    Dong, Pingsha
    Wei, Zhigang
    MARINE STRUCTURES, 2022, 84
  • [34] Data-driven control by using data-driven prediction and LASSO for FIR typed inverse controller
    Suzuki, Motoya
    Kaneko, Osamu
    ELECTRONICS AND COMMUNICATIONS IN JAPAN, 2023, 106 (03)
  • [35] Data-Driven Control by using Data-Driven Prediction and LASSO for FIR Typed Inverse Controller
    Suzuki M.
    Kaneko O.
    IEEJ Transactions on Electronics, Information and Systems, 2023, 143 (03) : 266 - 275
  • [36] A learning algorithm for a Data-driven Controller based on Fictitious Reference Iterative Tuning
    Wakitani, Shin
    Yamamoto, Tofu
    2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2016, : 4908 - 4913
  • [37] CONSTRAINTS TO GUARANTEE GAIN AND PHASE MARGINS FOR DATA-DRIVEN CONTROLLER TUNING METHODS
    Sakatoku, Taiga
    Yubai, Kazuhiro
    Yashiro, Daisuke
    Komada, Satoshi
    JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN, 2020, 28 (05): : 385 - 393
  • [38] Data-Driven Scenario Optimization for Automated Controller Tuning With Probabilistic Performance Guarantees
    Paulson, Joel A.
    Mesbah, Ali
    IEEE CONTROL SYSTEMS LETTERS, 2021, 5 (04): : 1477 - 1482
  • [39] Limited-complexity controller tuning: A set membership data-driven approach
    Valderrama, Freddy
    Ruiz, Fredy
    EUROPEAN JOURNAL OF CONTROL, 2021, 58 (58) : 82 - 89
  • [40] A Consideration on Approximation Methods of Model Matching Error for Data-Driven Controller Tuning
    Matsui Y.
    Ayano H.
    Masuda S.
    Nakano K.
    SICE Journal of Control, Measurement, and System Integration, 2020, 13 (06) : 291 - 298