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IEEE International Conference on Systems, Man and Cybernetics

作者： Amir Hosein Ghods Ashkan Rahimi Kian Masoud Tabibi Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Civil Engineering University of Amir-Kabir Tehran Iran

This paper proposes a fuzzy control approach for the traffic-responsive ramp metering and variable speed limits control, in order to reduce the peak-hour congestion on freeways. The objective of control is to minimize the total time spent in the traffic network. To ease the calibration process of fuzzy controller and improve the overall performance of ramp metering and variable speed limits, genetic algorithm is applied for tuning the fuzzy sets parameters. In order to evaluate the controller's efficiency and applicability, a comparison is made with traditional ALINEA based controller and the fuzzy ramp metering only case by a case-study. The macroscopic traffic model METANET and its extension for dynamic speed limits are used for optimization procedure and presenting the simulation results. The paper concludes that genetic fuzzy control of ramp metering and speed limits is expected to enhance the performance of the freeway traffic network.

关键词： Traffic control Fuzzy control Communication system traffic control Electric breakdown Fuzzy logic Fuzzy sets Nonlinear control systems Velocity control Safety Fuzzy systems

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A Bayesian approach to conceptualization using reinforcement learning

A Bayesian approach to conceptualization using reinforcement...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Saeed Amizadeh Majid Nili Ahmadabadi Babak N. Araabi Roland Siegwart Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran Iran ASL ETHZ Zurich Switzerland

Abstraction provides cognition economy and generalization skill in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction which maps the continuous state and action spaces into entities called concepts. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the Bayesian framework is proposed. This approach exploits and extends the mirror neuron's role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, an agent sequentially learns the concepts from both of its successes and its failures through interaction with the environment. These characteristics as a whole distinguish the proposed learning algorithm from positive sample learning. Simulation results show the correct formation of concepts' distributions in perceptual space in addition to benefits of utilizing both successes and failures in terms of convergence speed as well as asymptotic behavior. Experimental results, on the other hand, show the applicability and effectiveness of our method for a real robotic task such as wall-following.

关键词： Bayesian methods Learning Mirrors Neurons Decision making Orbital robotics Uncertainty Encoding Biological information theory Signal processing

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An Agent Matching Architecture for Bilateral Contacts in Multi Agent Markets

An Agent Matching Architecture for Bilateral Contacts in Mul...

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IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Hamed Kebriaei Vahid Johari Majd Ashkan Rahimi-Kian Intelligent Control Systems Lab. Electrical Engineering Dept. Tarbiat Modares University Tehran Iran Intelligent Control Systems Laboratory Electrical Engineering Department Tarbiat Moalem University Tehran Iran Control and Intelligent Process Center of Excellence Electrical Engineering Department University of Tehran Tehran Tehran Iran

In this paper, an agent matching architecture for bilateral contracts of a multi agent market is proposed. The bilateral market contains a number of sellers and buyers who face opponents for negotiation and signing bilateral contracts. Each agent has a hierarchical representation of trading commodity attributes, where based on that it will create a tree structure of attributes. Using this structure, we apply a modified version of fuzzy similarity algorithm to compute similarity between each pair of buyer and seller agents' trees. Then, we use game theory and the concept of Stackelberg equilibrium to assess the matchmaking among the seller and buyer agents. Agents who lose contract or want to sign a new contract have a chance to restart the procedure. Through a case study for bilateral contracts in energy market, the applicability of our agent matching architecture is illustrated.

关键词： Contracts Game theory Tree data structures Decision making Contacts Intelligent control Intelligent agent control systems Supply and demand Fuzzy sets

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A Sliding Mode controller for Switched-Reluctance Motor with Iterative Learning Compensation

A Sliding Mode Controller for Switched-Reluctance Motor with...

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International Conference on electrical Machines and Systems 2007(ICEMS2007)(第十届国际电机与系统会议)

作者： Milad Falahi Farzad R. Salmasi Control & Intelligent Processing Center of Excellence Electrical Engineering Department University of TehranP.O.Box 14395-515 Tehran Iran

In this paper, a direct torque sliding mode controller (SMC) is applied to the switched reluctance motor. This controller is robust to load torque and parameter changes. The major problem of sliding mode controller is the chattering in the output which will result in an increase in torque ripples. An iterative learning compensator is proposed to reduce the torque ripple. The iterative learning rule is updated in each iteration. This compensator enhances the performance of the controller to a great extent. The load torque is estimated using a linear Luenberger filter. The simplicity and high performance of the proposed scheme makes it desirable for implementations. The drive achieves satisfactory torque, speed tracking. The functionality of the proposed controller has been tested and verified using simulations.

关键词： Sliding mode control Reluctance motors Torque control Strontium Mathematical model control systems Error correction Robust control Machine learning Process control

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The Analysis of Saturation and Coreloss Effects of Induction Motor Direct Starting

The Analysis of Saturation and Coreloss Effects of Induction...

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International Conference on electrical Machines and Systems 2007(ICEMS2007)(第十届国际电机与系统会议)

作者： T. A. Najafabadi S.M Nabavi Control & Intelligent Processing Center of Excellence Electrical Engineering Department University of Tehran P.O.Box 14395-515 Tehran Iran

Induction motor direct starting may cause faults on both the power net and mechanical load. In other words the first current peak and the first torque peak of induction motors are vital for the power net and mechanical load respectively. A model for induction motors is proposed in [1] considering saturation effects. Although this model attempts to predict the induction motor behavior in direct starting, some differences between simulation and experimental results are perceived. In this paper, firstly the coreloss is taken into account in [1], and then we show that power net voltage initial phase value is impressive in the value of the current first peak in direct starting. Finally, by considering the condition of [1], our method fits experimental results better than [1].

关键词： induction motor direct starting saturation core loss

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A Sensorless IM Adaptive Feedback Linearization control with Core loss and Rotor Resistances Estimation

A Sensorless IM Adaptive Feedback Linearization Control with...

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International Conference on electrical Machines and Systems 2007(ICEMS2007)(第十届国际电机与系统会议)

作者： T.A. Najafabadi F.R. Salmasi Control & Intelligent Processing Center of Excellence Electrical Engineering Department University of Tehran P.O.Box 14395-515 Tehran Iran

In this paper, a sensorless induction motor drive based on the adaptive feedback linearization control is presented, while core loss and rotor resistances are estimated. A nonlinear observer is developed to estimate the dq fluxes and rotor speed, in addition to rotor and core loss resistances. Adaptive feedback linearization control is used to track the reference speed. In order to derive the adaptation laws, a Lyapunov function is introduced. Theoretical analysis and simulation results show that estimated rotor and coreloss resistances converge to actual values, if persistent excitation condition is satisfied. Furthermore, flux, electromagnetic torque, and rotor speed are estimated and controlled successfully.

关键词： induction motor sensorless feedback linearization core loss estimation

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TRACKING control IN THE PRESENCE OF NONLINEAR DYNAMIC FRICTIONAL EFFECTS: ROBOT EXTENSION

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Asian Journal of control 2008年第3期1卷

作者： M. Feemster D.M. Dawson A. Behal W. Dixon Matthew Feemster received the B.S degree in Electrical Engineering from Clemson University Clemson South Carolina in December 1994. Upon graduation he remained at Clemson University and received the M.S. degree in Electrical Engineering in 1997. During this time he also served as a research/teaching assistant. His research work focused on the design and implementation of various nonlinear control algorithms with emphasis on the induction motor and mechanical systems with friction present. He is currently working toward his Ph.D. degree in Electrical Engineering at Clemson University. Darren M. Dawson was born in 1962 in Macon Georgia. He received an Associate Degree in Mathematics from Macon Junior College in 1982 and a B.S. Degree in Electrical Engineering from the Georgia Institute of Technology in 1984. He then worked for Westinghouse as a control engineer from 1985 to 1987. In 1987 he returned to the Georgia Institute of Technology where he received the Ph.D. Degree in Electrical Engineering in March 1990. During this time he also served as a research/teaching assistant. In July 1990 he joined the Electrical and Computer Engineering Department and the Center for Advanced Manufacturing (CAM) at Clemson University where he currently holds the position of Professor. Under the CAM director's supervision he currently leads the Robotics and Manufacturing Automation Laboratory which is jointly operated by the Electrical and Mechanical Engineering departments. His main research interests are in the fields of nonlinear based robust adaptive and learning control with application to electro-mechanical systems including robot manipulators motor drives magnetic bearings flexible cables flexible beams and high-speed transport systems. Aman Behal was born in India in 1973. He received his Masters Degree in Electrical Engineering from Indian Institute of Technology Bombay in 1996. He is currently working towards a Ph.D in Controls and Robotics at Clemson University. His research focuses on the control of no

In this paper, we extend the observer/control strategies previously published in [25] to an n -link, serially connected, direct drive, rigid link, revolute robot operating in the presence of nonlinear friction effects modeled by the Lu-Gre model. In addition, we also present a new adaptive control technique for compensating for the nonlinear parameterizable Stribeck effects. Specifically, an adaptive observer/controller scheme is developed which contains a feedforward approximation of the Stribeck effects. This feedforward approximation is used in a composite controller/observer strategy which forces the average square integral of the position tracking error to an arbitrarily small value. Experimental results are included to illustrate the performance of the proposed controllers.

关键词： Friction compensation nonlinear control

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A Study On Iterative Learning control With Adjustment Of Learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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A Hierarchical Face Identification System Based on Facial Components

A Hierarchical Face Identification System Based on Facial Co...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Mehrtash T Harandi Majid Nili Ahmadabadi Babak N. Araabi School of cognitive science (SCS) Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran

It is generally agreed that faces are not recognized only by utilizing some holistic search among all learned faces, but also through a feature analysis that aimed to specify more important features of each specific face. This paper addresses a novel decision strategy that efficiently uses both holistic and facial component (left eye, right eye, nose and mouth) feature analysis to recognize faces. The proposed algorithm uses the whole face features in the first step of recognition task. If the decision machine fails to assign a class (with high confidence) then the individual facial components are processed and the resulting information are combined with those obtained from the whole face to assign the output. Simulation studies justify the superior performance of the proposed method as compared to that of Eigenface method. Experimental results also show that the proposed system is robust against small errors in facial component extractor.

关键词： Face recognition Humans Facial features Nose Mouth Statistical learning Linear discriminant analysis Principal component analysis Object recognition Vectors

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A Hierarchical Clustering Based on Mutual Information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on Image Processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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