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Frequency-domain identification algorithms for servo systems with friction

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2002年第5期10卷 654-665页

作者： Chen, YY Huang, PY Yen, JY Natl Taiwan Univ Dept Elect Engn Taipei 10764 Taiwan ASUSTek Comp Inc Taipei Taiwan Natl Taiwan Univ Dept Mech Engn Taipei 10764 Taiwan

Mechanical devices usually come with undesirable nonlinearities, such as friction, backlashes, and saturations. Under the assumption of linear systems, the commonly seen identification schemes utilize sinusoidal excitation signals for parameter identification. However, the data needed for identification are unavoidably distorted by the aforementioned nonlinearities and the identification result may not be satisfactory. In this paper, binary test signals are used to perform identification, thus simplifying the behavior of friction. An identification method based on the difference of binary multifrequency excitation signals is proposed. The modified identification algorithm does not suffer from the problem of nonlinear distortions in the signal shape and is able to determine the nonlinear friction such that an accurate servo system model can be derived. A high-precision ball-screw table with asymmetric friction is identified as a test plant for this approach. The results prove that the method can be used very successfully.

关键词： binary multifrequency signal frequency domain friction compensation identification algorithms system identification

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On LPV System identification algorithms for Input-Output Model Structures and their Relation to LTI System identification⁎

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IFAC-PapersOnLine 2018年第15期51卷 1098-1103页

作者： Schulz, Erik Janda, Oliver Schultalbers, Matthias IAV GmbH Gifhorn38518 Germany BSH Hausgeräte GmbH Giengen89537 Germany

Two new identification algorithms for linear parameter-varying (LPV) input-output (IO) systems are introduced in this paper based on the ARMAX and BJ model structure, respectively. Both algorithms are applicable for the MIMO case and thus fill the gaps of existing LPV IO system identification theory. Moreover, the relation between identification of LPV and LTI models is pointed out and exploited for the new LPV ARMAX algorithm. Efficacy of the two new algorithms is shown in simulation examples. © 2018

关键词： identification (control systems) Linear systems Model structures Religious buildings identification algorithms identification for control Input output model Linear parameter varying LPV systems Simulation example System identification theory Toolboxes

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RECURSIVE OUTPUT ERROR identification algorithms THEORY AND EVALUATION

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AUTOMATICA 1980年第5期16卷 443-462页

作者： DUGARD, L LANDAU, ID Laboratoire d'Automatique de Grenoble E.N.S.I.E.G. B.P. 46 38402 St. Martin D'Heres France

Several recurisve algorithms for parametric identification of discrete time systems derived from Model Reference Adaptive System (M.R.A.S.) techniques are analysed. All these algorithms belong to the class of output error methods which have been very little discussed previously in the identification literature. These algorithms are analysed in the deterministic and stochastic environment using the Equivalent Feedback Representation (E.F.R.) and Ordinary Differential Equation (O.D.E.) methods respectively. A comparative evaluation of these algorithms is presented. This comparison is made also with respect to various widely used recursive algorithms belonging to the ‘equation error method’ (extended least squares, approximate maximum likelihood).

关键词： Adaptive systems computational methods convergence analysis identification algorithms output error method stochastic systems

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Weighted Stochastic Gradient identification algorithms for ARX models

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IFAC-PapersOnLine 2015年第28期48卷 1076-1081页

作者： Wu, Ai-Guo Dong, Rui-Qi Fu, Fang-Zhou Harbin Institute of Technology Shenzhen Graduate School Shenzhen518055 China

In this paper, weighted stochastic gradient (WSG) algorithms for ARX models are proposed by modifying the standard stochastic gradient identification algorithms. In the proposed algorithms, the correction term is a weighted term of the correction terms of the standard SG algorithm in the current and last recursive steps. In addition, a latest estimation based WSG (LE-WSG) algorithm is also established. The convergence performance of the proposed LE-WSG algorithm is then analyzed. It is shown by a numerical example that both the WSG and LE-WSG algorithms can possess faster convergence speed and higher convergence precision compared with the standard SG algorithms if the weighting factor is appropriately chosen. © 2015

关键词： Stochastic systems Parameter estimation Stochastic models ARX model Convergence performance Correction terms Faster convergence identification algorithms Stochastic gradient Stochastic gradient algorithms Weighting factors

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Fractional identification algorithms applied to thermal parameter estimation

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IFAC Proceedings Volumes 2009年第10期42卷 1316-1321页

作者： Jean-Denis Gabano Thierry Poinot Université de Poitiers Laboratoire d'Automatique et d'Informatique Industrielle Ecole Supérieure d'Ingénieurs de Poitiers 40 Avenue du Recteur Pineau 86 022 Poitiers Cedex France Tel: 33 5 49 45 35 06

In this paper, we present a simulator of the heat flow through a wall in order to validate and compare different fractional identification algorithms. Indeed, heat transfer in homogeneous medium obeys to diffusion phenomenon which can be modelled with the help of fractional systems. We use models based on an original fractional integrator whose order 1/2 acts only over a limited spectral band. Simulations of front face thermal characterization consisting in measuring the temperature at the surface of a material where a random heat flux is applied are carried out. These simulations allow to validate the ability of the proposed estimation algorithms not only to rebuild the surface temperature but also to identify with a good accuracy the heat conductivity and diffusivity. Monte Carlo simulations are also performed in order to provide statistical properties of the thermophysical parameter estimates.

关键词： Non-integer modelling Fractional systems Diffusive interface identification algorithms Output-error identification Parameter estimation

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CONVERGENCE ANALYSIS OF INSTRUMENTAL VARIABLE RECURSIVE SUBSPACE identification algorithms

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IFAC Proceedings Volumes 2006年第1期39卷 279-284页

作者： G. Mercère M. Lovera Laboratoire d'Automatique et d'Informatique Industrielle 40 avenue du recteur Pineau 86022 Poitiers France Phone: + 33 (0)5 49453509 Fax: +33 (0)5 49454034 Dipartimento di Elettronica e Informazione Politecnico di Milano Piazza Leonardo da Vinci 32 20133 Milano Italy Phone: + 39 02 23993592 Fax: +39 02 23993412

The convergence properties of a recently developed recursive subspace identification algorithm of the MOESP class are investigated in this paper. The algorithm operates on the basis of an extended instrumental variable (EIV) version of the propagator method for signal subspace estimation. It is proved that, under weak conditions on the input signal and the identified system, the considered recursive subspace identification algorithm converges to a consistent estimate of the propagator and, by extension, of the state space system matrices.

关键词： Subspace methods Recursive algorithms identification algorithms State space models Tracking systems Convergence analysis

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The Master-Slave Family of System identification algorithms: Theoretical Results and New Structures

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IFAC Proceedings Volumes 1996年第1期29卷 4610-4615页

作者： Roberto López Valcarce Fernando Pérez Departamento de Tecnologías de las Comunicaciones Universidad de Vigo 36200 Vigo Spain

We develop an analysis of the Master-Slave family of system identification algorithms for the white noise input case. The Master-Slave techniques may be seen as off-line schemes that construct a sequence of equation error optimization problems, each one based on the solution to the previous one. Some conditions for the fixed points of the iteration are given. An alternative scheme based on interpolation conditions is proposed.

关键词： Adaptive algorithms identification algorithms Lattice filters Convergence

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Parameter identification of FIR systems with binary-valued observations: when the event-driven communication mechanism encounters DoS attacks

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年

作者： Fan, Jiahua Jia, Ruizhe Zhang, Kun Guo, Jin Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Minist Educ Key Lab Knowledge Automat Ind Proc Beijing Peoples R China

With the development of technology, finite impulse response(FIR) is widely used in signal processing, communication and other fields, so it is of great significance to study its parameter identification problem. In the communication process of FIR systems, denial-of-service(DoS) attacks cause data packet loss, and the introduction of event-driven communication mechanism can reduce the sensitivity of the system to DoS attacks, however, the coupling of the two increases the difficulty of parameter identification. In this paper, we explore the problem of FIR parameter identification in the presence of DoS attacks and event-driven communication from the perspective of the defense side. Initially, the inadequacy of the original identification algorithm in estimating parameters during attacks is acknowledged. Compared to the original algorithm, the improved algorithm incorporates parameters of a model based on DoS attacks and analyzes its convergence performance. Subsequently, the trace of the asymptotic normal variance matrix of the improved identification algorithm is utilised as a metric to gauge the attack's effectiveness, leading to the design of an optimal attack strategy. Building upon the enhanced identification algorithm, adjustments are made to the event-driven mechanism, and an adaptive identification algorithm is formulated. The convergence performance of this algorithm is analysed, followed by the calculation of a communication rate aimed at optimisation based on selected indicators. Lastly, the paper concludes with an examination of the correctness and validity of the findings through numerical simulation.

关键词： Finite impulse response(FIR) DoS attacks event-driven communication identification algorithms

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Efficient algorithms for ordinary differential equation model identification of biological systems

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IET SYSTEMS BIOLOGY 2007年第2期1卷 120-129页

作者： Gennemark, P. Wedelin, D. Chalmers Univ Technol Dept Comp Sci & Engn SE-41296 Gothenburg Sweden

algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.

关键词： noise model selection Biology and medical computing physiological models differential equations General, theoretical, and mathematical biophysics model identification parameter estimation Numerical approximation and analysis ordinary differential equation Model reactions in molecular biophysics metabolic pathway molecular biophysics biological systems biology computing biochemistry heuristic search algorithm identification algorithms Differential equations (numerical analysis) Function theory, analysis Simulation, modelling and identification genetics genetic network

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Recursive algorithms for identification in closed loop: A unified approach and evaluation

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AUTOMATICA 1997年第8期33卷 1499-1523页

作者： Landau, ID Karimi, A Lab. d'Automatique de Grenoble ENSIEG 38402 Saint Martin d'Hères BP 46 France

A unified presentation of recursive algorithms for plant model identification in closed loop is given. From the basic formulation of the problem of finding the plant model that gives the best predictor for the closed-loop system, two families of algorithms using either a reparameterized predictor for the closed loop or a plant predictor operating on filtered data are presented, and their asymptotic properties are examined. Validation tests for the models identified in closed loop are proposed. A comparative evaluation of the various algorithms in simulation and on real-time experiments concludes the paper. (C) 1997 Elsevier Science Ltd.

关键词： identification algorithms closed-loop identification validation robustness stability convergence

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