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IEEE International Conference on Mechatronics and Automation

作者： Hyo-Sung Ahn K.L. Moore YangQuan Chen Center for Self-Organizing and Intelligent Systems CSOIS Department of Electrical and Computer Engineering Utah State University Logan UT USA Research and Technology Development Center Applied Physics Laboratory Johns Hopkins University Laurel MD USA Center for Self-Organizing and Intelligent Systems CSOIS Department of Electrical and Computer Engineering Utah State University Laurel UT USA

In iterative learning control, model uncertainty issue has been studied in various ways. But, in existing works, the model uncertainty has been considered as iteration invariant, that is, it does not change on the iteration axis. Furthermore, in these works, monotone convergence problem has not been properly studied. In this paper, new monotonic convergence condition of iteration varying model uncertain iterative learning control system is derived. So, the main goal of this paper is to guarantee the monotone convergence of the iteration varying model uncertainty system until the tracking error is reduced below the previously calculated base-line error boundary. Through the numerical example, the validity of the suggested method is illustrated.

关键词： Uncertainty Convergence intelligent systems Physics computing Error correction Laboratories Control system synthesis History Testing Equations

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Iterative MOESP Type Algorithm for Discrete Time Variant System Identification

Iterative MOESP Type Algorithm for Discrete Time Variant Sys...

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IEEE International Symposium on intelligent Control (ISIC)

作者： A.D.R. Tamariz C.P. Bottura G. Barreto Nucleus of Research and Development in Computer Science University Candido Mendes Rio de Janeiro Brazil UNICAMP School of Electrical and Computer Engineering Control and Intelligent Systems Laboratory State University of Campinas Sao Paulo Brazil

With the main objective to develop an iterative state-space identification algorithm for linear multivariable discrete time-variant systems, in this study we propose and implement a computational procedure we call MOESP-VAR that is based in subspace methods of the type "multivariable output-error state space" (MOESP). In addition the algorithm is tested with criterion and experimentation that we also propose

关键词： Iterative algorithms System identification Time varying systems Vectors Iterative methods Reactive power Testing State-space methods Control engineering Nonlinear systems

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Stability analysis of iterative learning control system with interval uncertainty

Stability analysis of iterative learning control system with...

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作者： Ahn, Hyo-Sung Moore, Kevin L. Chen, Yang Quan Center for Self-Organizing and Intelligent Systems Dept. of Electrical and Computer Engineering Utah State University 4160 Old Main Hill Logan UT 84322-4160 United States Research and Technology Development Center Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099 United States

ISBN: (纸本)008045108X

This paper presents a stability analysis of the iterative learning control (ILC) problem when the plant Markov parameters are subject to interval uncertainty. Using the super-vector approach to ILC, vertex Markov matrices are employed to develop sufficient conditions for both asymptotic stability and monotonic convergence of the ILC process. It is shown that Kharitonov segments between vertex matrices are not required for checking the stability of interval super-vector ILC systems, but instead checking just the vertex Markov matrices is sufficient. Copyright © 2005 IFAC.

关键词： Asymptotic stability

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Range identification for perspective dynamic systems with 3D imaging surfaces

Range identification for perspective dynamic systems with 3D...

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2005 American Control Conference, ACC

作者： Ma, Lili Chen, YangQuan Moore, Kevin L. 4160 Old Main Hill Logan UT 84322-4160 United States Center for Self-Organizing and Intelligent Systems Dept. of Electrical and Computer Engineering Utah State University 4160 Old Main Hill Logan UT 84322-4160 Research and Technology Development Center Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099

Range identification using image sequence via observations from a traditional camera-type vision system has been discussed in the literature. In this paper, the camera-type planar imaging surface is extended to several well-known 3D surfaces, such as an arbitrary plane, a sphere, an ellipsoid, and a paraboloid. For a general imaging surface, the resulting perspective dynamic system is not guaranteed to preserve an affine form. In this case, most existing nonlinear observers that are applicable to the perspective dynamic system observed via a camera can not be applied for the range identification problem directly. We show via simulations that our recently proposed linear approximation observer can perform the state estimation. © 2005 AACC.

关键词： Image segmentation

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Monotonic Convergent Iterative Learning Controller Design Based on Interval Model Conversion

Monotonic Convergent Iterative Learning Controller Design Ba...

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IEEE International Symposium on intelligent Control (ISIC)

作者： Hyo-Sung Ahn K.L. Moore YangQuan Chen Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering Utah State University Logan UT USA Research and Technology Development Center Applied Physics Laboratory Johns Hopkins University-Applied Physics Laboratory Laurel MD USA

This paper presents the design of a robust iterative learning controller for the case of a plant with interval model uncertainty in the "A-matrix" of its state space description. First order perturbation theory is utilized to find bounds on the eigenvalues and eigenvectors of the powers of A when A is an interval matrix. These bounds are then used for calculation of the interval uncertainty of the Markov matrix, which can then be used to design an iterative learning controller that ensures monotonic convergence for all systems in the interval plant

关键词： Uncertainty State-space methods Matrix converters Convergence Control systems Motion control intelligent systems Physics computing Design engineering USA Councils

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Effects of a STATCOM, a SCRC and a UPFC on the dynamic behavior of a 45 bus section of the Brazilian power system

Effects of a STATCOM, a SCRC and a UPFC on the dynamic behav...

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IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： G.K. Venayagamoorthy Y. del Valle S. Mohagheghi W. Qiao S. Ray R.G. Harley D.M. Falcao G.N. Taranto T.M.L. Assis Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA University of KwaZulu-Natal Durban South Africa Engenharia Electrica Universidade Federal de Rio de Janeiro Rio de Janeiro Brazil

Flexible AC transmission systems (FACTS) devices are power electronics based shunt or series devices that are connected to the power network and can improve the system performance and the quality of supply. Application of three types of FACTS devices, a STATCOM, a SCRC and a UPFC, is studied in a 45 bus multimachine power system, as part of the Brazilian power system. The system is exposed to various transient and dynamic disturbances, and the performance of the system reinforced with the FACTS devices is compared with the original system. Simulation results are provided that indicate the effectiveness of these FACTS devices in the overall system behaviour

关键词： Power system dynamics Automatic voltage control Power systems Power system transients Power system simulation Flexible AC transmission systems Power transmission lines PSCAD Power electronics Circuit faults

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Algebraic $H_infty$ Design of Higher-Order Iterative Learning Controllers

Algebraic $H_infty$ Design of Higher-Order Iterative Learnin...

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IEEE International Symposium on intelligent Control (ISIC)

作者： K.L. Moore YangQuan Chen Hyo-Sung Ahn Research and Technology Development Center Johns Hopkins University-Applied Physics Laboratory Laurel MD USA Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering Utah State University Logan UT USA

This paper presents an H infin -based design technique for synthesis of higher-order iterative learning controllers (ILC) for plants subject to iteration-domain input/output disturbances and plant model uncertainty. Using a formulation of the higher-order ILC problem into a high-dimensional MIMO discrete-time system, it is shown how the addition of input/output disturbances and plant model uncertainty to the ILC problem can be cast into a standard H infin framework. An algebraic approach to solving the problem in this framework is presented, resulting in a sub-optimal controller that can achieve both stability and robust performance. The key observation is that H infin synthesis can be used for higher-order ILC design to achieve reliable performance in the presence of iteration-varying external disturbances and model uncertainty

关键词： Uncertainty Robust stability Robustness MIMO Robust control Delay Paper technology Physics Laboratories intelligent systems

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Schur stability radius bounds for robust iterative learning controller design

Schur stability radius bounds for robust iterative learning ...

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American Control Conference (ACC)

作者： Hyo-Sung Ahn K.L. Moore YangQuan Chen Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering Utah State University Logan UT USA Research and Technology Development Center Johns Hopkins University-Applied Physics Laboratory Laurel MD USA

This paper computes bounds on the Schur stability radius for use in designing robust iterative learning controllers. The discrete Lyapunov equation is used to compute the Schur stability radius of the ILC system for the case of parametric or interval perturbations in the system Markov parameters. This paper is distinct from the existing ILC works in that the interval robustness concepts are integrated with asymptotic stability and monotonic convergence conditions to suggest design of the learning gain matrix that makes the system robust against interval model uncertainties up to a computable bound. After derivations of the analytical stability radius, optimization schemes are suggested to design the learning gain matrix. The proposed approach allows design of causal/noncausal time-varying learning matrix gains.

关键词： Robust stability Robust control Convergence Uncertainty Asymptotic stability Linear matrix inequalities intelligent systems Physics computing Equations Stability analysis

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STABILITY ANALYSIS OF ITERATIVE LEARNING CONTROL SYSTEM WITH INTERVAL UNCERTAINTY

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IFAC Proceedings Volumes 2005年第1期38卷 282-287页

作者： Hyo-Sung Ahn Kevin L. Moore YangQuan Chen Center for Self-Organizing and Intelligent Systems Dept. of Electrical and Computer Engineering 4160 Old Main Hill Utah State University Logan UT 84322-4160 USA Research and Technology Development Center Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099

关键词： Iterative learning control monotonic convergence interval uncertainty Schur stability vertex matrices

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Journal of Information Science and engineering: Editorial notice

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Journal of Information Science and engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

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