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H_∞ model reduction of linear continuous-time systems over finite-frequency interval

IET CONTROL THEORY AND APPLICATIONS 2010年第3期4卷 499-508页

作者： Du, X. Yang, G. -H. Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China Northeastern Univ Minist Educ Key Lab Integrated Automat Proc Ind Shenyang 110004 Liaoning Peoples R China

This article studies the H-infinity model reduction problem for linear continuous-time systems over a finite-frequency interval. Different from the existing methods in the literature, we resort to the aid of recently developed generalised Kalman-Yakubovich-Popov (GKYP) lemma. Based on a in-depth exploitation of the GKYP lemma and the Projection lemma, sufficient conditions for the finite-frequency H-infinity model reduction problems are derived and expressed in terms of solutions to a set of linear matrix inequalities (LMIs), which can be handled easily by using the available toolbox. Numerical examples are included for illustration.

关键词： H∞ control reduced order systems linear systems projection lemma Algebra generalised Kalman-Yakubovich-Popov lemma Optimal control Control system analysis and synthesis methods sufficient conditions linear continuous-time systems continuous time systems H∞ model reduction linear matrix inequalities finite-frequency interval

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Robust Fault Estimation Based on Proportional Differential (PD) Learning Observer for linear continuous-time systems with State time-varying Delay

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND systems 2022年第1期20卷 58-72页

作者： You, Fuqiang Wang, Chao Northeastern Univ Coll Informat Sci & Engineer 3 Wenhua Rd Shenyang 110819 Peoples R China

This article studies the fault estimation problem of linear systems with unkown state time-varying delay. A new PD learning observer (LO) is proposed to realize the simultaneous estimation of system states and actuator fault. Furthermore, the time-delay dependence criterion of the existence of the proposed observer is given through the technique of linear matrix inequalites (LMIs). Because the mismatch between the original system and the observer system in the time delay term has a bad influence on the fault estimation, the H-infinity performance index is introduced. At the same time, the problem of sensor fault estimation based on PD learning observer for time-delay systems is studied. Finally, three simulation examples are used to prove the effectiveness of the proposed method.

关键词： Fault estimation H-infinity performance index linear continuous-time systems linear matrix inequalities PD learning observer

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D-stability for linear continuous-time systems with multiple time delays

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AUTOMATICA 2006年第9期42卷 1589-1592页

作者： Mao, Wei-Jie Chu, Jian Zhejiang Univ Inst Adv Proc Control Natl Lab Ind Control Technol Hangzhou 310027 Peoples R China

This paper addresses the D-stability problem of linear continuous-time systems with multiple time delays. A sufficient condition to guarantee all poles of a given linear time-delay system located in the specified region is proposed. This condition can be solved using LMI toolbox. The result is also extended to the robust D-stability problem of perturbed linear time-delay systems. The illustrative example shows that this method is effective to check the D-stability of linear time-delay systems. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： linear continuous-time systems multiple time delays D-stability unstructured perturbation linear matrix inequality (LMI)

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Identifiability and Parameter Estimation of linear continuous-time systems under Irregular and Random Sampling

Identifiability and Parameter Estimation of Linear Continuou...

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17th IFAC Symposium on System Identification (SYSID 2015)

作者： Mu, Biqiang Guo, Jin Wang, Le Yi Yin, George Xu, Lijian Zheng, Wei Xing Chinese Acad Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Wayne State Univ Dept Math Detroit MI 48202 USA State Univ New York Farmingdale State Coll Dept Elect & Comp Engn Technol Farmingdale NY 11735 USA Univ Western Sydney Sch Comp Engn & Math Penrith NSW 2751 Australia

This paper considers the problem of identifiability and parameter estimation of single-input-single-output, linear, time-invariant, stable, continuous-time systems under irregular and random sampling schemes. Conditions for system identifiability are established under inputs of exponential polynomial types and a tight bound on sampling density. Identification algorithms of Gauss-Newton iterative types are developed to generate convergent estimate sequences. When the sampled output is corrupted by observation noises, input design, sampling times, and convergent algorithms are intertwined. Persistent excitation (PE) conditions for strongly convergent algorithms are derived. Unlike the traditional identification, the PE conditions under irregular and random sampling involve both sampling;times and input values. Under the given PE conditions, iterative and recursive algorithms are developed to estimate the original continuous-time system parameters. The corresponding convergence results are obtained. Several simulation examples are displayed to verify the theoretical results. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： linear continuous-time systems irregular sampling identifiability nonlinear least square estimation algorithms convergence and asymptotical normality

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On improving the convergence speed of linear continuous-time systems under constrained control: Sylvester equation approach

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INTERNATIONAL JOURNAL OF CONTROL 2014年第1期87卷 161-168页

作者： Baddou, Atmane Mandour, Ilham Ecole Natl Sci Appl LGII Agadir 80000 Morocco

A controller design methodology is proposed for linear continuous-time systems with asymmetrical constrained control. Because of the trade-off between the size of the domain of attraction and convergence speed, one can improve the convergence speed during the system evolution. The closed-loop system starts with bad performances and a large domain of attraction and ends with good performances near the origin. The objective of this work is to overcome some discontinuities resulting from a piecewise linear control law previously proposed to improve the convergence speed of a linear system during its evolution. The proposed method is based on the use of the Sylvester equation together with a quadratic Lyapunov function. It guaranties the improvement of the speed of convergence without any discontinuities on the control vector components and respecting always the imposed asymmetric constraints without saturations.

关键词： asymmetric constrained control linear continuous-time systems convergence speed improvement

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Computation of the Largest Constraint Admissible Set for linear continuous-time systems with State and Input Constraints

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IFAC Proceedings Volumes 2014年第3期47卷 5574-5579页

作者： Moritz Schulze Darup Martin Mönnigmann Automatic Control and Systems Theory Ruhr-Universität Bochum Bochum Germany

We present a new method for the approximation of the largest constraint admissible set (CAS) for linear continuous-time systems with state and input constraints. The CAS is the set of initial states for which the controlled system does not violate the input or state constraints. The presented approach is based on a suitable discretization of the continuous-time system. In fact, we will show that CAS in the continuous-time case can be computed analogously to the discrete-time case, given an appropriate sampling time was chosen. We stress that the computation of CAS for continuous-time systems is considerably more difficult than for discrete-time systems, since one has to guarantee that the system does not violate the constraints in between the sampling instances.

关键词： linear continuous-time systems constraints terminal control predictive control

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New design of fixed-interval smoother using covariance information in linear stochastic continuous-time systems

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APPLIED MATHEMATICS AND COMPUTATION 2003年第2-3期144卷 557-567页

作者： Nakamori, S Kagoshima Univ Dept Technol Fac Educ Kagoshima 8900065 Japan

This paper proposes recursive least-squares fixed-interval smoothing and filtering algorithms in linear continuous-time stochastic systems. The estimators require the information of the autocovariance function of the signal, the variance of white observation noise and the observed value. The autocovariance function of the signal is expressed in the form of the semi-degenerate kernel. (C) 2002 Elsevier Inc. All rights reserved.

关键词： Wiener-Hopf integral equation linear continuous-time systems recursive estimation covariance information Stochastic process

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Design of linear continuous-time stochastic estimators using covariance information in Krein spaces

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2001年第9期E84A卷 2261-2271页

作者： Nakamori, S Kagoshima Univ Fac Educ Kagoshima 8900065 Japan

This paper proposes new recursive fixed-point smoother and filter using covariance information in linear continuous-time stochastic systems. To be able to treat the stochastic signal estimation problem, a performance criterion, extended from the criterion in the H-infinity filtering problem by introducing the stochastic expectation, is newly introduced in this paper. The criterion is transformed equivalently into a minmax principle in game theory, and an observation equation in the Krein spaces is obtained as a result. For gamma (2) < infinity, the estimation accuracies of the fixed-point smoother and the filter are superior to the recursive least-squares (RLS) Wiener estimators previously designed in the transient estimation state. Here, gamma represents a parameter in the proposed criterion. This paper also presents the fixed-point smoother and the filter using the state-space parameters from the devised estimators using the covariance information.

关键词： filter fixed-point smoother linear continuous-time systems covariance information H-infinity estimators

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Kalman filtering for continuous-time systems with multiple delayed measurements

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IET SIGNAL PROCESSING 2008年第1期2卷 37-46页

作者： Lu, X. Zhang, H. Wang, H. Wang, W. Xie, L. Dalian Univ Technol Res Ctr Informat & Control Dalian 116023 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Shandong Univ Sci & Technol Kay Lab Robot & Intelligent Technol Shandong Prov Qingdao 266510 Peoples R China

The paper focuses on the Kalman filtering problem for linear continuous-time systems with multiple delayed measurements. An explicit and simpler solution to the Kalman filtering problem is presented for such systems. The approach applied is the reorganised innovation analysis. The obtained Kalman filter is given in terms of Riccati differential equations. A numerical example is given to demonstrate the proposed approach.

关键词： multiple delayed measurements Kalman filtering Riccati equations Riccati differential equations Active filters and other active networks linear continuous-time systems linear algebra (numerical analysis) Kalman filters continuous time filters

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Recursive estimation of impulse response function using covariance information in linear continuous stochastic systems

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APPLIED MATHEMATICS AND COMPUTATION 2002年第2-3期131卷 339-347页

作者： Nakamori, S Kagoshima Univ Fac Educ Dept Technol Kagoshima 890065 Japan

This paper proposes a new recursive least-squares (RLS) estimation algorithm for an impulse response function in linear continuous-time wide-sense stationary stochastic systems. It is assumed that the input signal to the unknown impulse response function is contaminated by additive white Gaussian observation noise. The output signal from the system related with the impulse response function is observed with additive white Gaussian noise. The impulse response function is estimated recursively in terms of the variance of the white Gaussian observation noise included in the input signal, the autocovariance function of the process before the observation noise is added to the input signal, the crosscovariance function between the output observed value and the input observed value, concerning the system based on the unknown impulse response function. (C) 2002 Elsevier Science Inc. All rights reserved.

关键词： impulse response function linear continuous-time systems recursive estimation covariance information stochastic process

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