This paper describes an unconstrained measurement system for scratching motion. We have proposed an unconstrained bed monitoring system by piezoceramic sensors that could detect heartbeat respiration and body movement...
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This paper describes an unconstrained measurement system for scratching motion. We have proposed an unconstrained bed monitoring system by piezoceramic sensors that could detect heartbeat respiration and body movement. The system could measure the bed vibrations due to the motion of the person on the bed. In this paper, we apply this unconstrained bed sensing method to the system for detecting scratching motion. The proposed sensors are placed under the bed feet. When the subject is lying on the bed, the output signals from the sensors are proportional to the magnitude of the vibration due to the body movement of the subject. Hence, it is possible to detect the subject's scratching motion from the output signals. Furthermore, also with output signals from the three ceramic sensors, the proposed system can detect the direction of the body movement, which enables us to estimate the scratched area. We evaluated three scratching motions using the proposed system in the validity experiment as follows: Case 1 is the subject's scratching the right cheek with right hand;Case 2 is the subject's scratching the back after turning over, and;Case 3 is the subject's scratching the shin with another foot. As the results of the experiment, we identified the scratching signals that enable the determination when the scratching occurred. Furthermore, the difference among the amplitudes of the output signals enabled us to estimate where the subject scratched.
The paper analyzed and model of a fault tolerant electromechanical controlled worm gear driven fuel shut off valve for aerospace application. The analysis is mainly on design a reduced order fractional controller. Thi...
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In vehicular ad-hoc networks (VANETs), moving vehicles carry data and exchange it as they pass each other. Storage in a vehicle, in other words, the amount of saved data is limited resource in general. Therefore, attr...
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This paper presents a decision support tool for the effectiveness of a Quality Management system (QMS) in a company. To develop this tool, a new approach PAHP based on the combination of the Pareto Optimality Concept ...
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In this paper, a method for hand posture recognition, which is robust for hand posture changing in an actual environment, is proposed. Conventionally, a data glove device and a 3D scanner have been used for the featur...
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This paper considers the Model Predictive control (MPC) set point tracking/regulation problem for a discrete LTI system, which is subject to a class of unbounded disturbances/tracking signals called extended constant ...
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This paper considers the Model Predictive control (MPC) set point tracking/regulation problem for a discrete LTI system, which is subject to a class of unbounded disturbances/tracking signals called extended constant signals of unknown structure. Examples of disturbances which belong to this class include constant disturbances as well as unbounded signals such as w[k]=√k and log (k), k=1,2,3,…. A discussion re the choice of window size for MPC is also made; in particular, it is shown that the window size must be larger than a certain lower bound, which can be easily determined, in order to guarantee closed loop stability in MPC control. The main contribution is a formulation of the system's plant equations under which, for output regulation, no knowledge of the structure or magnitude of disturbances is needed in order to achieve set point regulation for this class of extended constant signals. The result is of interest since it also implies that no disturbance observer is necessary in order to solve the set point tracking/regulation problem when full-state feedback is available. The results are experimentally verified.
In many industrial processes, the first order plus time delay (FOPDT) is still being widely used. FOPDT systems are also called “KLT systems (gain, delay, and time constant).” Considering uncertainties in the time d...
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In many industrial processes, the first order plus time delay (FOPDT) is still being widely used. FOPDT systems are also called “KLT systems (gain, delay, and time constant).” Considering uncertainties in the time delay, this paper attempts to answer this research question: “ Will a fractional order controller help and do better? “ In this paper, we first focus on fractional order proportional and integral controller (FOPI) for varying time-delay systems. Based on our previously proposed FOPI controller tuning rules using fractional M s constrained integral gain optimization (F-MIGO), we tried to simultaneously maximize the jitter margin and ITAE performance (minimize ITAE performance index) for a set of hundred KLT systems having different time-constants and time-delay values. We observed that the optimization results in enlarged jitter margin of all systems at expense of a slight decrease in ITAE performance of delay dominated systems. Further, the F-MIGO optimization based tuning rules were summarized by approximation of optimized gain parameters and fractional orders α of the FOPI controller. Simulation results are presented to verify the proposed new tuning rules for best jitter margin and ITAE performance.
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.
Data Modeling is an essential first step for data preparation in any data mining procedure. Conventional entity-relational (E-R) data modeling is lossy, irreproducible, and time-consuming especially when dealing with ...
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This paper presents a novel sequential learning neural network implementation of action dependent adaptive critics. Sequential learning neural networks provide a systematic way of adding neurons in response to new dat...
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