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Quantized output feedback control for nonlinear Markovian jump distributed parameter systems with unreliable communication links

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APPLIED MATHEMATICS AND COMPUTATION 2019年 353卷 371-395页

作者： Song, Xiaona Wang, Mi Song, Shuai Wang, Zhen Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China Nanjing Univ Sci & Technol Sch Automat Control Nanjing 210094 Jiangsu Peoples R China Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China

This paper investigates robust quantized output feedback control of nonlinear Markovian jump distributed parameter systems (MJDPSs) with incomplete transition rates. Considering the digital communication channel in practical applications, the data of measured output and control input is quantized before transmission, by mode-dependent quantizer. Furthermore, a randomly occurring communication fault phenomenon is noticed in stability analysis, and is described by Bernoulli distributed white sequences. Based on Takagi-Sugeno (T-S) fuzzy model and dynamic parallel distributed compensate principle, a novel output feedback controller is developed. The conditions, to ensure that the MJDPSs are stochastically stable with mixed L-2-L-infinity/H-infinity performance, are given in terms of linear matrix inequalities (LMIs), and controller gains can be obtained by LMI toolbox. Finally, an example is provided to illustrate the effectiveness of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： distributed parameter systems Incomplete transition rates Mode-dependent quantizer Markovian jump Output feedback

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SENSOR AND CONTROLLER LOCATION PROBLEMS FOR distributed parameter-systems

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AUTOMATICA 1979年第3期15卷 347-352页

作者： ICHIKAWA, A RYAN, EP UNIV WARWICK CTR CONTROL THEORYCOVENTRY CV4 7ALWARWICKSHIREENGLAND

Optimal location of point sensors and controllers in stochastic regulator problems for evolution equations is considered using a semigroup approach and the separation principle. The existence of an optimal location is established under very general hypotheses. The theory is illustrated by specializing to a diffusion equation and some computational results are presented.

关键词： computational methods computer-aided design Control theory distributed parameter systems functional analysis Kalman filters optimal control optimal filtering sensors

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Robust Control Guaranteeing Uniform Ultimate Boundedness for a Class of Mismatched Uncertain Parabolic distributed parameter systems

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND systems 2013年第4期11卷 728-733页

作者： Xing, Hai-Long Li, Dong-Hai Cui, Wen-Shan Qingdao Agr Univ Sci & Informat Coll Qingdao 266109 Peoples R China Tsinghua Univ Dept Thermal Engn State Key Lab Power Syst Beijing 100084 Peoples R China

The robust control problem for a class of mismatched uncertain parabolic distributed parameter systems is studied under conditions of the uncertain terms being norm-bounded. The proposed robust controller can guarantee the system states to be the uniform ultimate boundedness, and their bound can be arbitrarily small. And, the proposed control method is successfully applied to a typical diffusion-reaction process with uncertain terms.

关键词： Diffusion-reaction process distributed parameter systems robust control uncertain uniform ultimate boundedness

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FILTERING AND SMOOTHING FOR LINEAR DISCRETE-TIME distributed parameter-systems BASED ON WIENER-HOPF THEORY WITH APPLICATION TO ESTIMATION OF AIR-POLLUTION

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IEEE TRANSACTIONS ON systems MAN AND CYBERNETICS 1981年第12期11卷 785-801页

作者： OMATU, S SEINFELD, JH Department of Chemical Engineering California Institute of Technology Pasadena CA USA Department of Information Science and Systems Engineering University of Tokushima Tokushima Japan

Optimal filtering and smoothing algorithms for linear discrete-time distributed parameter systems are derived by a unified approach based on the Wiener-Hopf theory. The Wiener-Hopf equation for the estimation problems is derived using the least-squares estimation error criterion. Using the basic equation, three types of the optimal smoothing estimators are derived, namely, fixed-point, fixed-interval, and fixed-lag smoothers. Finally, the results obtained are applied to estimation of atmospheric sulfur dioxide concentrations in the Tokushima prefecture of Japan.

关键词： Filtering theory Nonlinear filters Smoothing methods distributed parameter systems Air pollution Gaussian processes Filtering algorithms Equations Chemical engineering Chemical technology

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Approximate Controllability and Approximate Observability of Singular distributed parameter systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第5期65卷 2294-2299页

作者： Ge, Zhaoqiang Ge, Xiaochi Zhang, Jifeng Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China City Univ London Cass Business Sch London EC1Y 8TE England Chinese Acad Sci Inst Syst Sci Acad Math & Syst Sci Beijing 100190 Peoples R China

Necessary and sufficient conditions for the approximate controllability and approximate observability of a singular distributed parameter system are obtained in the sense of distributional solution. These general results are used to examine the approximate controllability and approximate observability of the Dzektser equation in the Theory of Seepage.

关键词： Controllability distributed parameter systems Observability Germanium Space heating Mathematical model Hilbert space Approximate controllability approximate observability distributional solution singular distributed parameter systems

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REMARKS ON COMPUTING COPRIME FACTORS FOR distributed parameter-systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1992年第5期37卷 626-630页

作者： WU, NE Department of Electrical Engineering State University of New York Binghamton NY USA

This note presents an indirect method for computing coprime factors for a class of linear time-invariant distributed parameter systems. The method overcomes the numerical instability of an existing direct decomposition method. It is shown that coprime factorization can be reduced to constructing simultaneously stabilizing finite dimensional controllers, for which an efficient iterative procedure is developed.

关键词： distributed computing distributed parameter systems Riccati equations Matrices State estimation Matrix decomposition Polynomials Nonlinear filters Filtering

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Variable structure sliding mode control for a class of uncertain distributed parameter systems with time-varying delays

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INTERNATIONAL JOURNAL OF CONTROL 2009年第2期82卷 287-297页

作者： Xing, Hailong Gao, Cunchen Tang, Gongyou Li, Donghai Tsinghua Univ Dept Thermal Engn Beijing 100084 Peoples R China Ocean Univ China Coll Math Qingdao 266071 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266071 Peoples R China

This article considers sliding mode control of a class of parabolic linear uncertain distributed parameter systems with time-varying delays. The sliding mode controller design does not contain time delay terms and drives the state trajectory of the system to the sliding manifold in finite time. A sufficient condition of asymptotic stability for the sliding motion is derived. A simulation example is presented to illustrate effectiveness of the proposed method.

关键词： distributed parameter systems uncertain sliding mode control time-varying delays

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MEASUREMENT OPTIMIZATION WITH SENSITIVITY CRITERIA FOR distributed parameter-systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1980年第5期25卷 889-901页

作者： NAKAMORI, Y MIYAMOTO, S IKEDA, S SAWARAGI, Y UNIV TSUKUBA CTR SCI INFORMAT PROCSAKURAIBARAKI 30031JAPAN

We consider the problem of optimally designing sensors for observation of a class of distributed parameter systems. The design of sensors concerns the choice of measurement conditions so that the information provided by measurements is maximal. This problem has been posed as a deterministic optimal control problem for a system equation of the Riccati type which governs a filter covariance. In the present study we introduce a functional called a sensitivity criterion by extending the Fisher information matrix to function spaces. It is shown that maximizing this criterion leads to a suboptimal solution of the sensor design problem associated with an infinite-dimensional state estimation problem. The existence theorem for a type of measurement control problem is proved and some numerical results are presented.

关键词： distributed parameter systems Riccati equations Optimal control Control systems Filters Covariance matrix State estimation Sensor systems Noise measurement Nonlinear equations

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Closed-loop P-type Iterative Learning Control of Uncertain Linear distributed parameter systems

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IEEE/CAA Journal of Automatica Sinica 2014年第3期1卷 267-273页

作者： Xisheng Dai Senping Tian Yunjian Peng Wenguang Luo the School of Electrical and Information Engineering Guangxi University of Science and Technology the School of Information and Electrical Engineering Shandong University of Science and Technology the School of Automation Science and Engineering South China University of Technology

An iterative learning control problem for a class of uncertain linear parabolic distributed parameter systems is discussed,which covers many processes such as heat and mass transfer,convection diffusion and *** condition of allowing system state initially to have error in the iterative process a closed-loop P-type iterative learning algorithm is presented,and the sufficient condition of tracking error convergence in L2 norm is ***,the convergence of the tracking error in L2 and W1,2 space is proved by using Gronwall-Bellman inequality and Sobolev *** the end,a numerical example is given to illustrate the effectiveness of the proposed method.

关键词： Algorithm design and analysis Closed loop systems Iterative methods Learning (artificial intelligence) Uncertainty Iterative learning control closed-loop P-type learning algorithm distributed parameter system Plant uncertain distributed parameter systems Algorithm design and analysis Iterative learning control follow error Uncertainty Closed loop systems iterative methods

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Wireless Sensor Network-Based distributed Approach to Identify Spatio-Temporal Volterra Model for Industrial distributed parameter systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第12期16卷 7671-7681页

作者： Gupta, Saurav Sahoo, Ajit Kumar Sahoo, Upendra Kumar Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 India

The prodigious amount of data movement among sources, data centers, or processing elements precludes the utilization of least-squares (LS) and fusion-center (FC)-based modeling and control. The LS methods are offline in nature, hence may face difficulty in real-time implementation. FC-based methods that are nonrobust due to single point failure, require large communication bandwidth and computationally fast processing unit. To curb these limitations, this article identifies distributed parameter systems by estimating the parameters of spatio-temporal Volterra model using in-network data processing. It can handle the immense volume of data by distributing the processing tasks of FC among the wireless sensor network nodes. To facilitate distributed optimization, the global objective function is reformulated as a multiple constrained separable problem which is then decomposed into augmented Lagrangian form. Then, alternating direction method of multipliers along with coordinate descent method is employed to obtain the global optimal solution collaboratively. Further, a communication-efficient algorithm is designed for the proposed approach to deploy in an ad-hoc network. Simulations are carried out on two industrial distributed parameter systems (catalytic rod and tubular reactor) to illustrate the practicality of the proposed algorithm.

关键词： Wireless sensor networks Adaptation models Kernel Real-time systems Data models distributed parameter systems Estimation Alternating direction method of multipliers (ADMM) distributed parameter systems (DPSS) Karhunen-Loeve principal component analysis (KL PCA) nonlinear spatio-temporal wireless sensor network (WSN)

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