作者：
T. IwataM. YamakitaK. FurutaDept. of Mechanical and Environment Informatics
Tokyo Institute of Technology. Takaaki Iwata received the BE degrees in Mechanical Engineering in 1990 from Waseda University
Japan. From 1990 to 1996 he jointed NOK Co. Ltd. In 1996 he received his ME degrees in Mechanical and Environmental Informatics from Tokyo Institute of Technology. He is currently a PhD student of Tokyo Institute of Technology. He is a member of SICE RSJ JSME and IEEE. Dept. of Control and Systems Engineering
Tokyo Institute of Technology. Masaki Yamakita received Be
ME and PhD from Tokyo Institute of Technology in 1984 1986 and 1989 respectively. From 1989 he was a Research Associate in the Department of Control Engineering of Tokyo Institute of Technology and from 1993 he was a Lecturer at Tokyohashi University of Technology. He is currently an Associate Professor in the Department of Control and Systems Engineering of Tokyo Institute of Technology. His research interests include robotics learning control robust control and non-linear control. Katsuhisa Furuta was born in Tokyo
Japan in 1940. He received his B.S. M.S. and Ph.D. degrees in Chemical Engineering from Tokyo Institute of Technology in 1962 1964 and 1967 respectively. Since 1967 he has been a teaching staff of Tokyo Institute of Technology where he is currently a Professor of Graduate School of Information Science and Engineering. He is a fellow of IEEE and SICE. He is now a member of Science Council of Japan the secretary of the engineering division a council member of IFAC and the President of SICE in 1999.
In this paper, an improved version of online continuation method controller for PWM systems is presented. The online continuation method controller solves the PWM-type deadbeat control problem numerically. By using th...
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In this paper, an improved version of online continuation method controller for PWM systems is presented. The online continuation method controller solves the PWM-type deadbeat control problem numerically. By using this algorithm, iterations for solving the set of nonlinear equations always start from a good solution candidate and therefore it can be used online. The original continuation method is modified so that it is applicable to real systems. The validity of the proposed control method is demonstrated by a laboratory-scale flexible arm manipulator.
作者：
Omid ShakerniaYi MaT. John KooShankar SastryDept. 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-mot...
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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.
We propose a nanofixation method with melting/solidifications of a low melting metal based on nanomanipulation inside an electron microscope. Indium micro particles are experimentally used as the low melting metal. Fo...
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We propose in situ formation of gel microbeads made of a thermoreversible hydrogel for indirect laser micromanipulation of microorganisms, DNA and viruses. Irradiation,using a 1064 nm laser, of an aqueous solution mix...
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ISBN:
(纸本)1424407176
We propose in situ formation of gel microbeads made of a thermoreversible hydrogel for indirect laser micromanipulation of microorganisms, DNA and viruses. Irradiation,using a 1064 nm laser, of an aqueous solution mixed with pol-(N-isopropylacrylamide) through a high magnification lens resulted in the formation of a gel microbead at the laser focus due to heating The gel microbead was trapped by the laser and was used for indirect laser micromanipulation of microscale and nanoscale samples. Laser tweezers can typically handle a microscale object with size ranging from several tens of nm to several hundreds of pjm in a stable manner. However, a nanoscale object with a size of a few nm cannot be stably manipulated, and laser beam heating is a major problem. This paper shows a method of indirect manipulation of microorganisms, DNA and viruses using a gel microbead made from the poly-(N-isopropylacrylamide) aqueous solution. We succeeded in reducing the laser power for gel microbead formation, and in using the laser-trapped gel microbead for the manipulation of microorganisms, DNA and viruses.
Recurrent neural networks have dynamic characteristics and can express functions of time. The recurrent neural networks can be applied to memorize robotic motions, i.e. trajectory of a manipulator. For this purpose, i...
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Recurrent neural networks have dynamic characteristics and can express functions of time. The recurrent neural networks can be applied to memorize robotic motions, i.e. trajectory of a manipulator. For this purpose, it is necessary to determine appropriate interconnection weights of the network. Formerly, learning algorithms based on gradient search techniques have been shown. However, it is difficult for the recurrent neural network to learn such functions while using previous approaches because of much computing requirement and limitation of memory. This paper presents a new learning scheme for the recurrent neural networks by genetic algorithm (GA). The GA is applied to determine interconnection weights of the recurrent neural networks. The GA approach is compared with the backpropagation through time which is a famous learning algorithm for the recurrent neural networks. Simulations illustrate the performance of the proposed approach.< >
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