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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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Learning control: The state of the art and perspective

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Zidonghua Xuebao/Acta Automatica Sinica 2005年第6期31卷 943-955页

作者： Xu, Jian-Xin Hou, Zhong-Sheng Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117576 Singapore Singapore Advanced Control Systems Laboratory School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 China

The recent advances in learning control theory are reviewed. First insight is provided into the concept and principle associated with learning control under a repeatable control environment. Then a briefing is given to the two main streams of learning control-iterative learning control and repetitive control. Subsequently we survey several hot topics in the field of learning control. New trends in learning control using Lyapunov methods are also sketched, followed by remarks on future directions.

关键词： Learning systems

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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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Addressing to online adaptive controller malfunction in fault tolerant control

Addressing to online adaptive controller malfunction in faul...

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Proceedings of the 2004 IEEE International Symposium on intelligent control - 2004 ISIC

作者： Delima, Pedro G. Yen, Gary G. Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078-5032 United States

A complete Fault Tolerant control solution calls for a nonlinear adaptive controller with universal approximation capability and guaranteed stability. To fulfill this requirement we propose the use of Neural Networks trained online under a Globalized Dual Heuristic Programming architecture supervised by a decision logic capable of identifying controller malfunctions in early stages and providing new avenues with greater probability of convergence using information from a Dynamic Model Bank. The classification and distinction of controller malfunctions and of the faults in the system is achieved through three independent quality indexes. Proof-of-the-concept simulations of nonlinear plants demonstrate the approach legitimacy. © 2004 IEEE.

关键词： Fault tolerant computer systems

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Neural gain scheduling multiobjective genetic fuzzy PI control

Neural gain scheduling multiobjective genetic fuzzy PI contr...

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Proceedings of the 2004 IEEE International Symposium on intelligent control - 2004 ISIC

作者： Bottura, Celso Pascoli De Oliveira Serra, Ginalber Luiz Control and Intelligent Systems Laboratory School of Electrical and Computer Engineering State University of Campinas 400 Cid. Univ. Zeferino Vaz 13083-970 Campinas-SP Brazil

This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. Hence, the optimization problem is a multiobjective one, from which results an optimal fuzzy PI controller. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators. ©2004 IEEE.

关键词： Fuzzy control

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Addressing to online adaptive controller malfunction in fault tolerant control

Addressing to online adaptive controller malfunction in faul...

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IEEE International Symposium on intelligent control (ISIC)

作者： P.G. DeLima G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

A complete fault tolerant control solution calls for a nonlinear adaptive controller with universal approximation capability and guaranteed stability. To fulfill this requirement we propose the use of neural networks trained online under a globalized dual heuristic programming architecture supervised by a decision logic capable of identifying controller malfunctions in early stages and providing new avenues with greater probability of convergence using information from a dynamic model bank. The classification and distinction of controller malfunctions and of the faults in the system is achieved through three independent quality indexes. Proof-of-the-concept simulations of nonlinear plants demonstrate the approach legitimacy

关键词： Programmable control Adaptive control Fault tolerance Dynamic programming Logic programming Stability Neural networks Convergence Nonlinear dynamical systems control systems

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An evolutionary algorithm method for sampling n-partite graphs

An evolutionary algorithm method for sampling n-partite grap...

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Congress on Evolutionary Computation

作者： M.L. Goldstein G.G. Yen Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA

The growth of use of graph-structured databases modeled on n-partite graphs has increased the ability to generate more flexible databases. However, the calculation of certain features in these databases may be highly resource-consuming. This work proposes a method for approximating these features by sampling. A discussion of the difficulty of sampling in n-partite graphs is made and an evolutionary algorithm-based method is presented that uses the information from a smaller subset of the graph to infer the amount of sampling needed for the rest of the graph. Experimental results are shown on a publications database on Anthrax for finding the most important authors.

关键词： Evolutionary computation Sampling methods Spatial databases Ontologies Feature extraction Pattern recognition intelligent systems intelligent control Deductive databases control system synthesis

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Neural gain scheduling multiobjective genetic fuzzy PI control

Neural gain scheduling multiobjective genetic fuzzy PI contr...

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IEEE International Symposium on intelligent control (ISIC)

作者： C.P. Bottura G.Ld.O. Serra Control and Intelligent Systems Laboratory School of Electrical and Computer Engineering State University of Campinas Sao Paulo Brazil

This work proposes a gain scheduling adaptive control scheme based on fuzzy systems, neural networks and genetic algorithms for nonlinear plants. A fuzzy PI controller is developed, which is a discrete time version of a conventional one. Its data base as well as the constant PI control gains are optimally designed by using a genetic algorithm for simultaneously satisfying the following specifications: overshoot and settling time minimizations and output response smoothing. Hence, the optimization problem is a multiobjective one, from which results an optimal fuzzy Pl controller. A neural gain scheduler is designed, by the backpropagation algorithm, to tune the optimal parameters of the fuzzy PI controller at some operating points. Simulation results are shown to demonstrate the efficiency of the proposed structure for a DC servomotor adaptive speed control system used as an actuator of robotic manipulators.

关键词： Fuzzy control Pi control Adaptive control Genetic algorithms Algorithm design and analysis Optimal control Adaptive scheduling Fuzzy systems Neural networks Minimization methods

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Receding horizon finite memory controls for output feedback controls of discrete-time state space models

Receding horizon finite memory controls for output feedback ...

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Annual Conference of Industrial Electronics Society

作者： Soohee Han Wook Hyun Kwon Control Information Systems Laboratory School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea

ISBN: (纸本)0780387309

In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter. The stability of the RHFMC is investigated for stochastic systems.

关键词： Output feedback State-space methods control systems Stochastic systems Linearity State feedback Optimal control Linear feedback control systems Finite impulse response filter Stability

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A novel controller for a power system based belbic (brain emotional learning based intelligent controller)

A novel controller for a power system based belbic (brain em...

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Proceedings of the Biannual World Automation Congress

作者： R. Mohammdi-Milasi C. Lucas B. Najar-Arrabi Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran Tehran Iran Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Engineering School of Intelligent Systems (SIS) Institute for Studies in Theoretical Physics and Mathematics (IPM) University of Tehran Tehran Iran

In this paper we report the utilization of a novel controller (BELBIC) based un emotion processing mechanism in brain for power system. Using the BELBIC controller, both transient stability and voltage regulation of power systems are achieved. The special characteristic of this controller that makes it effective is its flexibility its five gain parameters that give good freedom for choosing favorite response. With this degree of freedom choosing of these parameters involves trade-off between overshoot and speed of response. The effectiveness of the proposed BELBIC controller is shown through some computer simulations on a (SMIB) power system.

关键词： control systems Power system control Power systems Power system stability Power system modeling Power system transients Power system faults Power system dynamics Voltage control Process control

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