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2008 American control Conference (ACC), vol.5

作者： Johannes Maess Andrew J. Fleming Frank Allgower Institute for Systems Theory and Automatic Control Universität Stuttgart Stuttgart Germany School of Electrical Engineering and Computer Science University of Newcastle Callaghan NSW Australia

The operation speed and tracking accuracy of piezoelectric tube actuators in scanning probe microscopy is significantly reduced due to the excitation of the scanner eigenfrequencies by the driving voltages. Feedback control is a suitable method for vibration suppression but suffers from the required additional sensor equipment and high cost for generation of a displacement feedback signal. Operating the piezotube in single-electrode excitation mode allows the comparatively uncomplex measurement of the induced voltages at sensor electrodes. The Finite Element (FE) approach enables an accurate modeling of the voltage signals. Furthermore, geometrical details included in the dynamic FE-model are the sample mass attached to the top of the tube as well as tube eccentricity and dislocated electrodes. Additionally, the FE-model accounts for dynamics-coupling effects along the separate axes of the three-dimensional tube motion. Comparison with experimental data demonstrates the accuracy of the FE-model. The controller employing the induced voltage as a feedback signal is designed by means of a reduced order model obtained from modal truncation. The efficacy of the controller is illustrated by closed-loop simulations.

关键词： Electrodes Piezoelectric actuators Scanning probe microscopy Feedback control Vibration control Costs Signal generators Finite element methods Voltage control Signal design

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Optimal Measurement Feedback control of Finite-time Continuous Linear systems

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IFAC Proceedings Volumes 2008年第2期41卷 15339-15344页

作者： Natalia Dmitruk Rolf Findeisen Frank Allgöwer Institute of Mathematics National Academy of Sciences of Belarus Surganovstr. 11 220072 Minsk Belarus Institute for Automation Engineering Otto-von-Guericke Universität Magdeburg Universitätsplatz 2 39016 Magdeburg Germany Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 70550 Stuttgart Germany

We consider optimal control of linear continuous time systems subject to constrained inputs based on output measurements that are subject to set-membership uncertainties. The objective is to steer the system in a fixed finite-time to a given polyhedral set while minimizing a linear terminal cost function. To achieve this objective a min-max optimal control strategy is proposed, taking into account that at future time instants new measurement information is available for feedback. The proposed strategy coincides with the (computationally intractable) exact min-max dynamic programming solution if all future measurement times are considered. Limiting the number of instants at which the new measurement information is considered we achieve a compromise between the computational efforts for feedback construction and the performance of the closed-loop.

关键词： optimal control linear system constraints continuous time measurements set-membership uncertainty feedback computational algorithm

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Observability Based Parameter Identifiability for Biochemical Reaction Networks

Observability Based Parameter Identifiability for Biochemica...

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2008 American control Conference (ACC), vol.5

作者： D. Geffen R. Findeisen M. Schliemann F. Allgower M. Guay Department of Chemical Engineering Queen''s University Canada Institute for Automation Engineering Otto von Guericke Universität Magdeburg Magdeburg Germany Institute of Cell Biology and Immunology University of Stuttgart Germany Institute for Systems Theory and Automatic Control (IST) University of Stuttgart Germany

In systems biology, models often contain a large number of unknown or only roughly known parameters that must be identified. This work examines the question of whether or not these parameters can in fact be estimated from available measurements. We consider identifiability of unknown parameters in biochemical reaction networks obtained from first-principles-modeling of metabolic and signal transduction networks. Such systems consist of continuous time, nonlinear differential equations. Several methods exist for answering the question of identifiability for such systems;many of which restate the question of identifiability as one of observability. We consider the application of such methods to a representative biological system: the NF-(kappa)B signal transduction pathway. It is shown that existing observability based strategies, which rely on finding an analytical solution, require significant simplifications to be applicable to systems biology problems which are often not feasible. For this reason, a new method based on the use of an 'empirical observability Gramian' for checking identifiability is proposed. This method is demonstrated through the use of a simple biological example.

关键词： Observability systems biology Biological systems Biological system modeling Parameter estimation Biological control systems Signal processing Differential equations Optimization methods State-space methods

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Model predictive control of continuous time nonlinear differential algebraic systems

Model predictive control of continuous time nonlinear differ...

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作者： Sjöberg, Johan Findeisen, Rolf Allgöwer, Frank Division of Automatic Control Department of Electrical Engineering Linköpings Universitet Sweden Institute for Systems Theory and Automatic Control University of Stuttgart Germany

ISBN: (纸本)9783902661289

This work presents a sampled-data nonlinear model predictive control scheme with guaranteed stability for differential-algebraic systems. The main challenge is the avoidance of impulsive solutions due to discontinuities in the input or its derivatives. This is guaranteed by requiring the applied input to be sufficiently smooth. Stability is guaranteed by a terminal penalty term, together with a terminal region constraint. The control scheme is illustrated considering the control of an electrical circuit. Copyright © 2007 IFAC.

关键词： Model predictive control

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PRESIDENT'S PAGE

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Naval Engineers Journal 2009年第6期109卷

作者： Joseph F. Yurso President Joseph F. Yurso:is currently director of technical development for Q.E.D. Systems Inc. He received a Bachelors of Science in mechanical engineering from The Pennsylvania State University and an Masters of Science in mechanical engineering from the Naval Postgraduate School. He also completed a special management program at Carnegie Mellon University. Prior to entering the Navy as an engineering duty officer Mr. Yurso was a refrigeration and air conditioning engineer for the Bureau of Ships. His Navy experience includes engineer officer of a combatant ship submarine type desk quality assurance officer and production officer in naval shipyards and planning officer quality assurance officer and deputy supervisor in Supervisor of Shipbuilding offices. His two command tours were as Supervisor of Shipbuilding Groton Connecticut (1980-81) and Shipyard Commander Portsmouth Naval Shipyard Kittery Maine (1981-84). He was awarded the Legion of Merit for both of these tours. Mr. Yurso has been a member of ASNE since 1964. He is a life member and a sustaining member of the Society. He has been active in several Sections including Charleston Northern and Southern New England and Tidewater. He is a former chairman of the Tidewater Section and was the chairman of the ASNE Fleet Maintenance Symposium in 1991. He served as Council vice president from 1993 to 1996 and as a member of Council for six years. He was a contributing author to the Society's first edition of Naval Engineering and American Sea Power. In 1993 he was awarded the Frank G. Law Award for his dedication and longtime service to the Society. Mr. Yurso maintains memberships in other societies including the American Society of Mechanical Engineers Sigma Xi the American Society for Quality Control Naval Institute and Tidewater Association of Service Contractors. He has held registered professional engineer status in two states and is listed inWho's Who in Science and Engineering.

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MODEL PREDICTIVE control OF CONTINUOUS TIME NONLINEAR DIFFERENTIAL ALGEBRAIC systems

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IFAC Proceedings Volumes 2007年第12期40卷 48-53页

作者： Johan Sjöberg Rolf Findeisen Frank Allgöwer Division of Automatic Control Department of Electrical Engineering Linköpings Universitet Sweden Institute for Systems Theory and Automatic Control University of Stuttgart Germany

关键词： Predictive control nonlinear systems differential-algebraic systems descriptor systems singular systems stability

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Multi-agent system consensus in packet-switched networks 9

Multi-agent system consensus in packet-switched networks

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2007 9th European control Conference, ECC 2007

作者： Munz, Ulrich Papachristodoulou, Antonis Allgower, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart70550 Germany Department of Engineering Science University of Oxford Parks Road OxfordOX1 3PJ United Kingdom

ISBN: (纸本)9783952417386

In this paper, we consider the problem of consensus reaching in multi-agent systems exchanging information over digital networks. We first show how to model stochastic packet delays in such networks in a distributed delay framework. We discuss the validity of the approximation and we give conditions that justify this transformation. Subsequently, we present a control law for multi-agent systems (MAS) that guarantees consensus over networks with stochastic packet delays for both fixed and switching topologies. For fixed topologies, the underlying graph has to be connected and undirected. For switching topologies, we require in addition that the graphs be regular. © 2007 EUCA.

关键词： Multi agent systems

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NONLINEAR NETWORK STRUCTURES FOR FEEDBACK control

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Asian Journal of control 2008年第4期1卷

作者： F.L. Lewis Dept. of Electical Engineering The University of Texas at Arlington U.S.A. F. L. Lewis was born in Wärzburg. Germany subsequently studyning in Chile and Goruonstoun School in Scotland. He obtained the Bachelor's Degree in Physics/Electrical Engineering and the Master's of Electrical Engineering Degree at Rice University in 1971. He spent six years in the U.S. navy serving as Navigator aboard the frigate USS Trippe (FF-1075) and Executive Officer and Acting Commanding Officer aboard USS Salinan (ATF-161). In 1977 he received the Master's of Science in Aeronautical Engineering from the University of West Florida. In 1981 he obtained the Ph.D. degree at The Georgia Institute of Technology in Atlanta where he was employed as a professor from 1981 to 1990 and is currently an Adjunct Professor. He is a Professor of Electrical Engineering at The University of Texas at Arlington where he was awarded the Moncrief-O'Donnell Endowed Chair in 1990 at the Automation and Robotics Research Institute. Dr. Lewis has studied the geometric analytic and structural properties of dynamical systems and feedback control automation. His current interests include robotics intelligent control neural and fuzzy systes nonlinear systems and manufacturing process control. He is the author/co-author of 2 U.S. patents 124 journal papers 20 chapters and encyclopedia articles 210 refereed conference papers seven books: Optimal Control Optimal Estimation Applied Optimal Control and Estimation Aircraft Control and Simulation Control of Robot Manipulators Neural Network Control High-Level Feedback Control with Neural Networks and the IEEE reprint volume Robot Control. Dr. Lewis is a registered Professional Engineer in the State of Texas and was selected to the Editorial Boards of International Journal of Control Neural Computing and Applications and Int. J. Intelligent Control Systems. He is the recipient of an NSF Research Initiation Grant and has been continuously funded by NSF since 1982. Since 1991 he has received $1.8 m

A framework is given for controller design using Nonlinear Network Structures, which include both neural networks and fuzzy logic systems. These structures possess a universal approximation property that allows them to be used in feedback control of unknown systems without requirements for linearity in the system parameters or finding a regression matrix. Nonlinear nets can be linear or nonlinear in the tunable weight parameters. In the latter case weight tuning algorithms are not straightforward to obtain. Feedback control topologies and weight tuning algorithms are given here that guarantee closed-loop stability and bounded weights. Extensions are discussed to force control, backstepping control, and output feedback control, where dynamic nonlinear nets are required.

关键词： Intelligent feedback control neural networks neural network structures basis function networks fuzzy control fuzzy-neural networks universal approximation adaptive nonparametric control learning control systems

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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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Simulation of Piezoelectric Tube Actuators by Reduced Finite Element Models for controller Design

Simulation of Piezoelectric Tube Actuators by Reduced Finite...

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American control Conference (ACC)

作者： Johannes Maess Andrew J. Fleming Frank Allgower Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany School of Electrical Engineering and Computer Science University of Newcastle Australia

This article presents an extended modeling approach for piezoelectric tube actuators combining finite element analysis (FEA) and model order reduction (MOR) techniques. The model includes the full coupling between motion in all axes, while the reduced model order, which depends mainly on the desired bandwidth and model accuracy, makes it a suitable basis for controller design purposes. Simulation results in the frequency domain are compared to measurements and show an excellent agreement in a wide frequency range. The increase of scan accuracy in closed-loop operation is demonstrated by simulation data of a model-based H infin controller.

关键词： Piezoelectric actuators Finite element methods Electrodes Atomic force microscopy Voltage Predictive models Motion control Frequency measurement Shape measurement Intelligent actuators

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