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IEEE Conference on control Technology and Applications (CCTA)

作者： Zhongzhou Chen Qing Zheng Zhiqiang Gao Department of Chemical Engineering University of Massachusetts Amherst Amherst MA USA The Center for Advanced Control Technologies Department of Electrical and Computer Engineering Cleveland State University Cleveland OH USA

In this paper a novel control strategy, the active disturbance rejection control (ADRC), is applied to the representative process control problems. In the ADRC framework, the disturbance and unmeasured dynamics associated with chemical processes are treated as an additional state variable, which is then estimated and compensated for in real time. This reduces a normally complex, time-varying, nonlinear, and uncertain dynamic process to an approximately linear, time-invariant, cascade-integral form, where a simple proportional-derivative (PD) controller suffices. Simulation studies are performed on two nonlinear continuous stirred tank reactors (CSTR), both demonstrate very good performance in the absence of an accurate mathematical model of the process.

关键词： Process control Chemical processes control systems State estimation Proportional control PD control Three-term control Pi control Aerospace control Nonlinear dynamical systems

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Initial Rotor Position Estimation for Sensorless Brushless DC Drives

Initial Rotor Position Estimation for Sensorless Brushless D...

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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： P. Champa P. Somsiri P. Wipasuramonton P. Nakmahachalasint Industrial Control and Automation Lab National Electronics and Computer Technology Center Thailand Industrial Control and Automation Lab National Electronics and Computer Technology Center Thailand Department of Electrical Engineering Thammasat University Thailand

This paper presents a method to determine the initial rotor position of a brushless DC machine at standstill without a position sensor. The key principle of the rotor position estimation is based on the simple detection and comparison of phase voltage and current responses relating to the stator inductance varied with the position of the rotor magnet. In the proposed method, only three pulse voltage injections are applied and 30 degree resolution can be achieved. Moreover, no knowledge of machine parameters is required. The effectiveness of the proposed method is validated by experimental results.

关键词： Voltage Rotors Phase detection Position measurement Current measurement Inductance measurement Drives Phase estimation Stators Brushless DC motors

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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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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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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR control

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Asian Journal of control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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A parametric Lyapunov equation approach to the design of low gain feedback

A parametric Lyapunov equation approach to the design of low...

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26th Chinese control Conference, CCC 2007

作者： Zhou, Bin Duan, Guangren Lin, Zongli Center for Control Theory and Guidance Technology Harbin Institute of Technology P.O. Box 416 Harbin Heilongjiang 150001 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia P.O. Box 400743 Charlottesville VA 22904-4743 United States

ISBN: (纸本)7900719229

Low gain feedback has found several applications in constrained control systems, robust control and nonlinear control. Low gain feedback refers to a family of stabilizing state feedback gains that are parameterized in a scalar and go to zero as the scalar decreases to zero. Such feedback gains can be constructed either by an eigenstructure assignment algorithm or through the solution of a parametric algebraic Riccati equation (ARE). The eigenstructure assignment approach leads to feedback gains in the form of a matrix polynomial in the parameter, while the ARE approach requires the solution of an ARE for each value of the parameter. This paper proposes an alternative approach to low gain feedback design based on the solution of a parametric Lyapunov equation. Such an approach possesses the advantages of both the eigenstructure assignment approach and the ARE based approach. It also avoids the possible numerical stiffness in solving a parametric ARE and the structural decomposition of the open loop system that is required by the eigenstructure assignment approach.

关键词： Feedback

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A new design of robust H∞ filters for uncertain discrete-time state-delayed systems

A new design of robust H∞ filters for uncertain discrete-ti...

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IEEE Conference on Decision and control

作者： Huijun Gao Xiangyu Meng Tongwen Chen Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

This paper investigates the problem of H infin estimation for discrete-time systems with a time-varying delay in the state and parameter uncertainties residing in a polytope. A new filter design procedure is proposed, which is developed based on homogeneous polynomially parameter-dependent matrices of an arbitrary degree. A numerical example is given to illustrate the effectiveness and advantage of the proposed filter design methods.

关键词： Robustness Filtering Delay effects Filters Polynomials Space technology Robust control Delay estimation State estimation Time varying systems

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H∞ fuzzy control with missing data

H∞ fuzzy control with missing data

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huijun Gao Yan Zhao Tongwen Chen Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

This paper investigates the problem of H infin fuzzy control of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the control strategy takes the form of parallel distributed compensation. The communication links, existing between the plant and controller, are assumed to be imperfect (that is, data-packet dropouts occur intermittently, which appear typically in a network environment), and stochastic variables satisfying the Bernoulli random binary distribution are utilized to model the unreliable communication links. Attention is focused on the design of H infin controllers such that the closed- loop system is stochastically stable and preserves a guaranteed H infin , performance. Two approaches are developed to solve this problem, based on quadratic Lyapunov function and basis- dependent Lyapunov function respectively. Several examples are provided to illustrate the usefulness and applicability of the developed theoretical results.

关键词： Communication system control Fuzzy systems Fuzzy control control systems Lyapunov method Stability analysis Nonlinear systems control system synthesis Space technology Stochastic processes

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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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AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY CHARACTERISTICS

AUTOMATIC BRAIN TUMOR SEGMENTATION USING TISSUE DIFFISIVITY ...

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IEEE International Symposium on Biomedical Imaging

作者： Azadeh Yazdan-Shahmorad Hesamoddin Jahanian Suresh Patel Hamid Soltanian-Zadeh Radiology Department Henry Ford Health System Medical Image Analysis Laboratory Detroit MI USA Electrical and Computer Engineering Department Wayne State University Detroit MI USA Biomedical Engineering Department University of Michigan Ann Arbor MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department Faculty of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424406715;1424406714

Water diffusion measurements have been shown to be sensitive to tissue cellular size, extra cellular volume, and membrane permeability. Therefore, diffusion tensor imaging (DTI) by MRI can be used to characterize highly cellular regions of tumors versus acellular regions, distinguishing cystic regions from solid regions. An automatic segmentation method is proposed in this paper based on a multi-phase clustering algorithm to segment the brain tumors in a feature space extracted from DTI images. The algorithm is applied on images of a total of 20 patients with 4 different types of tumors. The tumor region segmentation was 92% accurate based on the segmentation results using anatomical images and 100% accurate based on biopsy results. In general, the segmentation results obtained by the proposed method revealed a strong agreement with the biopsy results and anatomical images, providing support for the accuracy and robustness of the proposed feature space and the segmentation procedure.

关键词： Neoplasms Image segmentation Diffusion tensor imaging Clustering algorithms Biopsy Permeability measurement Volume measurement Size measurement Biomembranes Magnetic resonance imaging

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