Non-asymptotic and robust estimation for fractional order pseudo-state space model using an algebraic parametric method

被引:2
|
作者
Wang, Jia-Chang [1 ,2 ]
Liu, Da-Yan [2 ]
Boutat, Driss [2 ]
Wang, Yong [1 ]
Wu, Ze-Hao [3 ]
机构
[1] Univ Sci & Technol China, Dept Automat, Hefei 230026, Peoples R China
[2] Univ Orleans, INSA Ctr Val Loire, PRISME EA 4229, F-18022 Bourges, France
[3] Foshan Univ, Sch Math & Big Data, Foshan 528000, Peoples R China
基金
中国国家自然科学基金;
关键词
Non-asymptotic estimation; Model-based differentiator; Fractional order pseudo -state space model; Algebraic parametric method; SYSTEMS; DIFFERENTIATORS; ALGORITHMS; NOISE;
D O I
10.1016/j.dsp.2022.103899
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper mainly investigates the derivative estimation problem for a class of fractional order linear systems. After applying the Laplace transform to the considered pseudo-state space representation model, a series of multiplications and derivations are applied to eliminate unknown initial conditions, which define an annihilator. Then, through strict mathematical derivation, an input-output integral equation is deduced when returning into the time domain. Based on the obtained equation and thanks to the fractional Leibniz formulas, algebraic integral formulas are provided, which can non-asymptotically and robustly estimate the fractional derivatives of the system output. Moreover, error analysis in discrete noisy cases is given to study the performance of the proposed differentiator. Finally, numerical simulations are provided to show the effectiveness of the proposed method.(c) 2022 Elsevier Inc. All rights reserved.
引用
收藏
页数:18
相关论文
共 50 条
  • [21] Nonasymptotic Fractional Derivative Estimation of the Pseudo-State for a Class of Fractional-Order Partial Unknown Nonlinear Systems
    Wang, Zhi-Bo
    Liu, Da-Yan
    Boutat, Driss
    Zhang, Xuefeng
    Shi, Peng
    IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (11) : 7392 - 7405
  • [22] On-line process identification using the Modulating Functions Method and non-asymptotic state estimation
    Byrski, Witold
    Drapala, Michal
    ARCHIVES OF CONTROL SCIENCES, 2022, 32 (03) : 535 - 555
  • [23] Robust State Estimation of Fractional-order Complex Networks with Parametric Uncertainties
    Chen Aimin
    Wang Xingwang
    Wang Junwei
    Liu Zhiguang
    Zhang Fengpan
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 396 - 401
  • [24] Non-asymptotic State Estimation of Linear Reaction Diffusion Equation using Modulating Functions
    Ghaffour, Lilia
    Noack, Matti
    Reger, Johann
    Laleg-Kirati, Taous-Meriem
    IFAC PAPERSONLINE, 2020, 53 (02): : 4196 - 4201
  • [25] A novel modulating functions-based non-asymptotic fractional order state differentiator for DC motor systems
    Wang, Lei
    Liu, Da-Yan
    Huang, Liang
    Gibaru, Olivier
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2024, 138
  • [26] Fractional Order State Space Canonical Model Identification Using Fractional Order Information Filter
    Safarinejadian, Behrouz
    Asad, Mojtaba
    2015 INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP), 2015, : 65 - 70
  • [27] Fractional order MPC design using improved state space model
    Zou, Qin
    Zhang, Junfeng
    Zhang, Ridong
    Gao, Furong
    Xue, Anke
    IFAC PAPERSONLINE, 2017, 50 (01): : 7535 - 7540
  • [28] Non-asymptotic state-space identification of closed-loop stochastic linear systems using instrumental variables
    Szentpeteri, Szabolcs
    Csaji, Balazs Csanad
    SYSTEMS & CONTROL LETTERS, 2023, 178
  • [29] Non-Asymptotic Neural Network-based State and Disturbance Estimation for a Class of Nonlinear Systems using Modulating Functions
    Marani, Yasmine
    N'Doye, Ibrahima
    Kirati, Taous Meriem Laleg
    2023 AMERICAN CONTROL CONFERENCE, ACC, 2023, : 3062 - 3068
  • [30] An online intelligent robust adaptive LSQR estimation method for LTI state space model
    Hosseini, Shahram
    Navabi, M.
    Hajarian, Masoud
    IET CONTROL THEORY AND APPLICATIONS, 2023, 17 (07): : 837 - 849