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recursive identification algorithms as Nonlinear Systems: Parameter Identifiability and Controllability

IFAC Proceedings Volumes 1989年第3期22卷 107-112页

作者： C.A. Schwartz H. Özbay Department CS/EE University of Vermont Burlington VT 05405-0156 USA Department EE University of Minnesota USA

In this paper we examine parameter identifiability of a system modeled by a linear regression (ARX model). We regard recursive identification algorithms, designed to estimate the parameters of the given linear model, as discrete time nonlinear systems. The output of a nonlinear system representing a recursive parameter estimation algorithm will be the vector of parameter estimates, and its input will be the observed data taken from the system being identified. First, we examine the nonlinear system and obtain a characterization of its controllability. We next link parameter identifiability of the ARX model with a probabilistic version of controllability of this nonlinear stochastic system. We obtain a necessary and sufficient condition for parameter identifiability of the model in a probabilistic context. This condition is given in terms of properties of the chosen algorithm.

关键词： Nonlinear Controllability recursive identification algorithms

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A new recursive identification algorithm for singularity free adaptive control

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SYSTEMS & CONTROL LETTERS 1998年第4期34卷 177-183页

作者： Prandini, M Campi, MC Univ Brescia Fac Ingn Dipartimento Elettron & Automat I-25123 Brescia Italy

Reportedly, guaranteeing the controllability of the estimated system is a crucial problem in adaptive control. In this paper, we introduce a least-squares-based identification algorithm for stochastic SISO systems, which secures the uniform controllability of the estimated system and presents closed-loop identification properties similar to those of the least-squares algorithm. The proposed algorithm is recursive and, therefore, easily implementable. Its use, however, is confined to cases in which the parameter uncertainly is highly structured. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： uniform controllability adaptive control least-squares identification stochastic systems recursive identification algorithms

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Convergence in Networked recursive identification with Output Quantization

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IFAC-PapersOnLine 2018年第15期51卷 915-920页

作者： Yasini, Sholeh Wigren, Torbjörn Division of Systems and Control Department of Information Technology Uppsala University UppsalaSE-751 05 Sweden

This paper analyzes conditions for global parametric convergence of a networked recursive identification algorithm. The FIR based algorithm accounts for networked delay and signal quantization. The paper constructs counterexamples to parametric convergence using low order dynamic models and an asymmetric binary quantizer. The associated ODEs of the first order model are analysed with analytical methods and by numerical simulation. In particular, the analysis proves that parametric convergence does not occur in case the input signal distribution is discrete taking a finite number of values, the problem is symmetric, or in case the input signal distribution, dynamic gain and switch point are such that there is no signal energy in an arbitrary small neighborhood of any switch point. Global parametric convergence is also proved for one of the low order cases. © 2018

关键词： Quantization (signal) Numerical methods Signal processing Analytical method Finite number First order models recursive identification recursive identification algorithms Signal distribution Signal energy Signal quantization

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Model estimation and controller reduction: Dual closed loop identification problems

Model estimation and controller reduction: Dual closed loop ...

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39th IEEE Conference on Decision and Control

作者： Landau, ID Karimi, A UJF ENSIEG CNRS INPGLab Automat Grenoble F-38402 St Martin Dheres France

ISBN: (纸本)0780366387

algorithms for direct controller reduction by identification in closed loop has been recently proposed [9, 6]. In this paper it is shown that the plant model identification in closed loop using closed loop output error identification algorithms and the direct estimation in closed loop of a reduced order controller feature a duality character. Basic schemes., algorithms and properties of the algorithms can be directly obtained by interchanging the plant model and the controller. Experimental results concerning the use of these algorithms for direct controller reduction can be found in a companion paper [7]. In the last part of the paper the interaction between plant model identification in closed loop and direct controller reduction is emphasized.

关键词： identification in closed loop controller reduction recursive identification algorithms asymptotic properties

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recursive parameter estimation algorithms

Recursive parameter estimation algorithms

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16th International Symposium of the Danube-Adria-Association-for-Automation-and-Manufacturing

作者： Navratil, P. Bobal, V

ISBN: (纸本)9783901509469

In adaptive control the ARX regression model is mostly utilized for description of given plant dynamics and unknown parameters are estimated by recursive least square method Unknown disturbances and non-modeled dynamics can cause that the recursive least squares method leads to inadequate estimations. The controller based on these estimated parameters can give poor performance. This paper deals with several well-known recursive identification methods for parameter estimation of ARX ARMAX OE (output-error) model in order to improve the self-tuning controller performance and reliability.

关键词： recursive estimation ARX models ARMAX models recursive identification algorithms forgetting factors

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MULTIESTIMATION SCHEME FOR ADAPTIVE CONTROL OF THREE TANK SYSTEM

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IFAC Proceedings Volumes 2007年第13期40卷 151-156页

作者： Petr Navrátil Vladimír Bobál Department of Process Control Faculty of Applied Informatics Tomas Bata University in Zlin Nad Stránemi 4511 760 05 Zlín Czech Republic fax : +420 57 603 5279

This paper deals with a one possibility of improvement of a self-tuning controller reliability and performance. A simple estimation scheme is replaced by so-called a multiestimation scheme and the self-tuning controller is then synthesized from this scheme. A higher level switching structure between various estimation schemes is used to supervise the reparametrization of the self-tuning controller in real time. The basic usefulness of the proposed scheme is to improve the accuracy of estimated parameters of the controlled system and then better transient response is obtained.

关键词： Self-tuning control recursive estimation ARX models ARMAX models recursive identification algorithms Multiestimation scheme

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