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Highly efficient parameter estimation algorithms for Hammerstein non-linear systems

IET CONTROL THEORY AND APPLICATIONS 2019年第4期13卷 477-485页

作者： Mao, Yawen Ding, Feng Xu, Ling Hayat, Tasawar Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266042 Peoples R China King Abdulaziz Univ Dept Math Nonlinear Anal & Appl Math NAAM Res Grp Fac Sci Jeddah 21589 Saudi Arabia

Hammerstein system identification is difficult because there exist the product items of the parameters between the non-linear block and the linear block. This study presents a novel parameter separation based recursive least squares (PS-RLS) identification algorithm for resolving this problem. Its basic idea is to use a linear filter to filter the output data and the noise, and then to obtain two new identification submodels in each of which the output is linear in the corresponding parameter vector. Compared with the over-parametrisation based recursive least squares method, the proposed algorithm can avoid estimating the redundant parameters and has a higher computational efficiency. The simulation results show that the proposed PS-RLS algorithm can generate highly accurate parameter estimates with less computational effort.

关键词： least squares approximations parameter estimation nonlinear control systems parameter separation parameter vector parameter estimation algorithms over-parametrisation based recursive least squares method identification submodels linear filter recursive least squares identification algorithm linear block nonlinear block Hammerstein system identification Hammerstein nonlinear systems highly accurate parameter PS-RLS algorithm

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Induction Motor Stator Fault Detection by a Condition Monitoring Scheme Based on parameter estimation algorithms

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第10期44卷 1138-1148页

作者： Duan, Fang Zivanovic, Rastko London South Bank Univ Sch Engn 103 Borough Rd London SE1 0AA England Univ Adelaide Sch Elect & Elect Engn Adelaide SA Australia

This article presents a simple, low-cost, and effective method for the early diagnosis of stator short-circuit faults. The approach relies on the combination of an induction motor mathematical model and parameter estimation algorithm. The kernel of the method is the efficient search for the characteristic parameters that indicate stator short-circuit faults. However, the non-linearity of a machine model may imply multiple local minima of an objective function implemented in the estimation algorithm. Taking this into consideration, the suitability of two industry-proven optimization algorithms (pattern search algorithm and genetic algorithm) as applied in the proposed condition monitoring method was investigated. Experimental results show that the proposed diagnosis method is capable of detecting stator short-circuit faults and estimating level and location of faults. The study also indicates that the proposed method is robust to motor parameters offset and unbalanced voltage supply. Application of the pattern search algorithm is suitable for a continuous monitoring system, where the previous result can be used as starting point of the new search. The genetic algorithm requires longer computation time and is suitable for the offline diagnostic system. It is not sensitive to the starting point, and achieving global solution is guaranteed.

关键词： induction motor condition monitoring stator fault detection parameter estimation algorithms

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Performance limitations of adaptive parameter estimation and system identification algorithms in the absence of excitation

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AUTOMATICA 1996年第4期32卷 549-560页

作者： Tsakalis, KS Arizona State University Center for Systems Science and Engineering Box 877606 Tempe AZ 85287-7606 U.S.A.

We address the problem of fundamental performance limitations in adaptive parameter estimation and system identification, occurring in environments where perturbations are present but there is lack of sufficient excitation. We construct a simple yet general bursting scenario to derive an analytical lower bound on the worst-case peak steady-state error for a wide class of parameter estimation and system identification algorithms. Our results show that in the absence of any input constraints, arbitrarily small perturbations impose a serious performance limitation, in the sense that the worst-case performance deteriorates proportionally with the size of the parametric uncertainty set.

关键词： parameter estimation algorithms system identification algorithms persistent excitation burst phenomena performance limits time-varying systems

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parameter estimation of frequency hopping signals based on analogue information converter

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IET COMMUNICATIONS 2019年第13期13卷 1886-1892页

作者： Zhi, Li Zhou Jianhua Hao, Chen Xu, Gou Jian, Li Sichuan Univ Minist Educ Key Lab Wireless Power Transmiss 24 South Sect 1Yihuan Rd Chengdu Sichuan Peoples R China China Shipbldg Ind Syst Engn Res Inst 1 Fengxian East Rd Beijing Peoples R China

Aiming at the shortcomings of the existing frequency hopping signal parameter estimation algorithms based on compressed sensing theory, such as weak anti-noise performance, high data transmission and processing, and high hardware implementation cost, this study proposes an improved frequency hopping signal parameter estimation algorithm based on analogue information converter called Baseband parameter estimation (BPE) method. BPE does not reconstruct the frequency hopping signal into the Nyquist spectrum, and after obtaining the support set of the signal, the parameter estimation of the frequency hopping signal is performed directly at the baseband. The processing of baseband spectral slices can convert unreconstructed signal parameter estimates into parameter estimates for certain support sets, which makes BPE possible. Simulation experiments show that compared with the existing parameter estimation algorithm, BPE can not only greatly reduce the bandwidth and storage pressure required for subsequent transmission, but also has good anti-noise performance.

关键词： compressed sensing frequency hop communication parameter estimation frequency hopping signals parameter estimation algorithms Nyquist spectrum storage pressure baseband parameter estimation unreconstructed signal parameter estimates BPE method hardware implementation cost weak anti-noise performance analogue information converter

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OPTIMAL parameter-estimation FOR BASIC PROBLEMS OF ECONOMIC OPERATION IN ELECTRIC-POWER SYSTEMS

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OPTIMAL CONTROL APPLICATIONS & METHODS 1981年第3期2卷 269-287页

作者： ELHAWARY, ME MANSOUR, SY UNIV ALBERTA DEPT ELECT ENGNEDMONTON T6G 2M7ALBERTACANADA

The paper treats the problem of optimal parameter estimation for some basic models commonly used in conjunction with optimal economic operation studies in integrated electric power systems. Three well-known parameter estimation algorithms are considered for this purpose. The weighted least-squares, Gauss-Newton, and Powell's regression algorithms are utilized to obtain optimal parameters for the fuel cost model of thermal generating sources, active power loss model for the electric network, as well as the active-reactive power loss models. Performance comparisons using some test systems show that the Powell's regression results provide satisfactory optimal estimates.

关键词： Electric power systems Optimal parameter estimation parameter estimation algorithms

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Operating points estimation of three-phase induction machines using a torque-speed tracking technique

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IET ELECTRIC POWER APPLICATIONS 2011年第3期5卷 307-316页

作者： Jaramillo-Matta, A. Guasch-Pesquer, L. Martinez-Salamero, L. Barrado-Rodrigo, J. A. Univ Rovira & Virgili Escola Tecn Super Engn Dept Engn Elect Elect & Automat Tarragona 43007 Spain

An algorithm implementing a behavioural modelling technique for estimating the parameters of induction machine models is proposed. This technique, called torque-speed tracking, starts from manufacturer data and solves the identified problems which are caused by the lack of operating points and by the method used to minimise the torque error. The proposed technique is implemented for both single-and double-cage models, and the obtained behaviour is compared with that one obtained with other techniques used for estimating model parameters. The proposed technique is validated both with the catalogue data of 64 induction machines, and with experimental data of a 75 kW induction machine. All torque errors obtained with the proposed technique were significantly smaller than those ones obtained with the referenced techniques, for both analysed models. This technique can be easily implemented in parameter estimation algorithms which are based solely on manufacturer data.

关键词： Asynchronous machines torque error asynchronous machines single-cage models parameter estimation algorithms behavioural modelling technique three-phase induction machines torque double-cage models parameter estimation operating points estimation power 75 kW torque-speed tracking technique

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UNCERTAINTY EVALUATION FOR ESTIMATES FROM RECURSIVE PROJECTION algorithms

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 1995年第1期9卷 97-106页

作者： BELFORTE, G Dipartimento di Autornatica e Informatica Politecnico di Torino. Corso Duca degli Abruzzi 24 I-10129 Torino. Italy

In this paper we consider parameter estimation of linear systems described by y(i) = a(i)(T) theta+e(i), where the ith measurement y(i) is linearly dependent on the parameter vector theta is an element of R(p) through the regressor vector a(i)(T) is an element of R(p) amd the measurement error e(i) is unknown but bounded. Some properties of previously presented algorithms for recursive parameter identification in the unknown but bounded error (UBBE) context are discussed. In particular it is analysed how different levels of information on the error structure can influence the choice of the identification algorithms and the possibility of evaluating the reliability of the estimates. Attention is also focused on the influence that forgetting schemes have on the estimates and on the confidence evaluation.

关键词： SET THEORETIC estimation UNKNOWN-BUT-BOUNDED UNCERTAINTY parameter estimation algorithms RECURSIVE estimation CONFIDENCE REGIONS

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Performance analysis of the generalised projection identification for time-varying systems

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IET CONTROL THEORY AND APPLICATIONS 2016年第18期10卷 2506-2514页

作者： Ding, Feng Xu, Ling Zhu, Quanmin Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China Wuxi Vocat Inst Commerce Sch Internet Things Technol Wuxi 214153 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY Avon England

The least mean square methods include two typical parameter estimation algorithms, which are the projection algorithm and the stochastic gradient algorithm, the former is sensitive to noise and the latter is not capable of tracking the time-varying parameters. On the basis of these two typical algorithms, this study presents a generalised projection identification algorithm (or a finite data window stochastic gradient identification algorithm) for time-varying systems and studies its convergence by using the stochastic process theory. The analysis indicates that the generalised projection algorithm can track the time-varying parameters and requires less computational effort compared with the forgetting factor recursive least squares algorithm. The way of choosing the data window length is stated so that the minimum parameter estimation error upper bound can be obtained. The numerical examples are provided.

关键词： time-varying systems least mean squares methods parameter estimation stochastic processes gradient methods performance analysis time-varying systems least mean square methods parameter estimation algorithms stochastic gradient algorithm time-varying parameters generalised projection identification algorithm stochastic process theory generalised projection algorithm forgetting factor recursive least squares algorithm data window length minimum parameter estimation error

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parameter-IDENTIFICATION FOR MODELS WITH BOUNDED ERRORS IN ALL VARIABLES

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SYSTEMS & CONTROL LETTERS 1992年第6期19卷 425-428页

作者： BELFORTE, G POLITECN TORINO DIPARTIMENTO AUTOMAT & INFORMAT I-10128 TURIN ITALY

In this paper, the problem of parameter identification for models with bounded measurement errors both on the input and on the output is addressed and some corrections to previously published results are presented. In particular, it is shown that only parameter overbounds can in general be computed for systems of the form y = (PHI + deltaPHI)theta + deltay when the bounded measurement errors deltaPHI and deltay are correlated. Since ARMAX and bilinear systems can be represented in this form, it turns out that tight parameter bounds are in general not available for these systems. Finally, we show that it is possible to check a posteriori whether the obtained bounds are tight or not.

关键词： ROBUST IDENTIFICATION SET-THEORETIC estimation UNKNOWN BUT BOUNDED ERRORS ERROR CORRELATION parameter estimation algorithms CONFIDENCE REGIONS parameter OVERBOUNDING

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BLOCK-SEQUENTIAL algorithms FOR SET-THEORETIC estimation

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SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS 1988年第4期9卷 513-527页

作者： PEARSON, RK

A new algorithm is proposed for solving the set-theoretic parameter estimation problem. In contrast to point estimation strategies like least squares or maximum likelihood, the set-theoretic parameter estimation problem imposes bounds on model errors and seeks the resulting bounds imposed on the free model parameters. The exact solution to this problem is a convex polytope in the parameter space with too many vertices for an exact solution to be practical. Thus, the standard solution approach is to seek an outer bounding set that is more easily parameterized. This paper describes a computational approach that interpolates in estimation efficiency and computational effort between two extreme cases described by other authors. Two simple numerical examples are included.

关键词： 52A40 62F25 93B30 set-theoretic estimation unknown-but-bounded uncertainty parameter estimation algorithms simultaneous linear inequalities confidence regions

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