Real-time Identification System Research Articles

Structural damage diagnosis based on on-line recursive stochastic subspace identification

This paper presents a recursive stochastic subspace identification (RSSI) technique foron-line and almost real-time structural damage diagnosis using output-only measurements.Through RSSI the time-varying natural frequencies of a system can be identified. Toreduce the computation time in conducting LQ decomposition in RSSI, the Givensrotation as well as the matrix operation appending a new data set are derived. Therelationship between the size of the Hankel matrix and the data length in each shiftingmoving window is examined so as to extract the time-varying features of the systemwithout loss of generality and to establish on-line and almost real-time systemidentification. The result from the RSSI technique can also be applied to structuraldamage diagnosis. Off-line data-driven stochastic subspace identification was usedfirst to establish the system matrix from the measurements of an undamaged(reference) case. Then the RSSI technique incorporating a Kalman estimator isused to extract the dynamic characteristics of the system through continuousmonitoring data. The predicted residual error is defined as a damage feature andthrough the outlier statistics provides an indicator of damage. Verification ofthe proposed identification algorithm by using the bridge scouring test data andwhite noise response data of a reinforced concrete frame structure is conducted.

Real-time nonlinear structural system identification via iterated unscented Kalman filter

Structural system identification has attracted much attention in the structural dynamics field over the past decades. The unscented Kalman filter (UKF) is often used to deal with nonlinear system identification in civil engineering field. In practices, applying a UKF to highly nonlinear structural systems is not a trivial task, particularly those subject to severe loading. Recently, a new technique, the iterated unscented Kalman filter (IUKF) is applicable to highly nonlinear systems. In this paper, the IUKF is applied for nonlinear structural system identification (NSSI). Experimental results show that the IUKF produces better state estimation and parameter identification than the UKF, and the IUKF is also more robust to measurement noise levels.

A novel 3D sensory system for robot-assisted mapping of cluttered urban search and rescue environments

In this paper, the first application of utilizing a unique 3D sensor for sequential 3D map building in unknown cluttered urban search and rescue (USAR) environments is proposed. The sensor utilizes a digital fringe projection and phase shifting technique to provide real-time 2D and 3D sensory information of the environment. The proposed sensor is unique over current technologies in that high-resolution 3D information of rubble filled environments can be acquired from the single sensor at a speed of 30 frames per second (fps). Furthermore, we propose the development of a novel robust and reliable landmark identification technique that utilizes both 2D and 3D depth images taken by the sensor for 3D mapping. Preliminary experiments show the potential of the real-time 3D sensory system and landmark identification scheme for robotic 3D mapping in unknown cluttered USAR-like environments.

The Algorithm Research of Coin Detection Based on DSP

With the widespread using of coins, in order to solve the troubles brought by counterfeit money, it is of great significance to develop a system for real-time identification of coins. In the analysis of image processing technology, the hardware and software in this field, this paper has verified the recognition algorithm in the MATLAB and VC, and it had set up an coin image processing system with the DSP chip as a platform. In this system not only the surface information of the coin can be achieved and processed in real time, but also a practical hardware and software basis for the further application of DSP for image processing can be built.

Real-Time System Identification of a Nonlinear Four-Story Steel Frame Structure—Application to Structural Health Monitoring

Structural health monitoring (SHM) is a means of identifying damage from structural response to environmental loads. Real-time SHM is of particular use for rapid assessment of structural safety by owners and civil defense authorities. This paper presents an algorithm for real-time SHM during earthquake events using only acceleration measurements and infrequently measured displacement motivated by global positioning system. The algorithm identifies a nonlinear baseline model including hysteretic dynamics and permanent deformation using convex integral-based fitting methods and piecewise linear least squares fitting. The methodology identifies pre and postyield stiffness, elastic and plastic components of displacement, and final residual displacement. It thus identifies key measures of damage affecting the immediate safety or use of the structure and the long-term cost of repair and retrofit. The algorithm is tested with simulated response data using the El-Centro earthquake record and with measured response data. Both data sets are based on a four-story nonlinear steel frame structure using the El-Centro ground motion record. Overall, the algorithm is shown to provide accurate indications of the existence, location, and magnitude of structural damage for nonlinear shear-type buildings. Additionally, the identified permanent displacement is a particularly useful damage measure for the construction of probabilistic fragility functions.

Real-Time Propagation Measurement System and Scattering Object Identification by 3D Visualization by Using VRML for ETC System

In the early deployment of electric toll collecting (ETC) system, multipath interference has caused the malfunction of the system. Therefore, radio absorbers are installed in the toll gate to suppress the scattering effects. This paper presents a novel radio propagation measurement system using the beamforming with 8-elmenet antenna array to examine the power intensity distribution of the ETC gate in real time without closing the toll gates that are already open for traffic. In addition, an identification method of the individual scattering objects with 3D visualization by using virtual reality modeling language will be proposed and the validity is also demonstrated by applying to the measurement data.

Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach

In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive to dynamic behaviour and robust to environmental variations. This task of accurate modelling has been achieved with a multi-network architecture. The multi-network with dynamic selection technique allows a combination of online and offline neural network models to be used in the architecture where the most suitable outputs are selected based on a given criterion. The neural network models are based on the autoregressive technique. The online network uses a novel training scheme with memory retention. Flight test validation results for online and offline models are presented. The multi-network dynamic selection technique has been validated on real-time hardware in the loop (HIL) simulation and the results show the superiority in performance compared to the individual models.

실시간 조종미계수 추정에 의한 무인비행기 조종면 고장검출

In case of an abnormal condition of control surface, the real-time estimation of aerodynamic derivatives are required for the reconfigurable control system to be flight for missions or return to the head office. The goal of this paper is to represent a technique of fault detection to the control surface as a base research to the fault tolerant control system for safety improvement of UAV. The real-time system identification for the fault detection to the control surface was applied with the recursive Fourier Transform and verified through the HILS and flight test. The failures of the control surface are detected by comparing the control derivatives in fault condition with the normal condition. As a result from the flight test, we have confirmed that the control derivatives of fault condition less than about 50% in the normal condition.

Person identification system for future digital tv with intelligence

Intelligent digital TV (iDTV) is a future digital TV with intelligence which can automatically provide user-personalized services for each audience. For the user-personalized services, the iDTV should recognize audiences in real-time. In this paper, we define a novel structure of the iDTV and propose a real-time person identification system in the iDTV that analyzes captured images and recognizes audiences. The proposed system consists of three processing units: preprocessing for reducing computational costs of the proposed system, face detection using a statistical approach, and face recognition using support vector machines (SVMs). Experimental results show that the proposed system achieves efficient performance with high recognition accuracy of 90% or higher at the speed of 15~20 fps, which is suitable for the iDTV

Application of the unscented Kalman filter for real-time nonlinear structural system identification

Over the past few decades, structural system identification based on vibration measurements has attracted much attention in the structural dynamics field. The well-known extended Kalman filter (EKF) is often used to deal with nonlinear system identification in many civil engineering applications. In spite of that, applying an EKF to highly nonlinear structural systems is not a trivial task, particularly those subject to severe loading. Unlike the EKF, a new technique, the unscented Kalman filter (UKF) is applicable to highly nonlinear systems. In this paper, the EKF and UKF are compared and applied for nonlinear structural system identification. Simulation results show that the UKF produces better state estimation and parameter identification than the EKF and is also more robust to measurement noise levels. Copyright © 2006 John Wiley & Sons, Ltd.

Real-time human identification using a pyroelectric infrared detector array and hidden Markov models

This paper proposes a real-time human identification system using a pyroelectric infrared (PIR) detector array and hidden Markov models (HMMs). A PIR detector array with masked Fresnel lens arrays is used to generate digital sequential data that can represent a human motion feature. HMMs are trained to statistically model the motion features of individuals through an expectation-maximization (EM) learning process. Human subjects are recognized by evaluating a set of new feature data against the trained HMMs using the maximum-likelihood (ML) criterion. We have developed a prototype system to verify the proposed method. Sensor modules with different numbers of detectors and different sampling masks were tested to maximize the identification capability of the sensor system.

EVOLVING FUZZY RULE-BASED SYSTEMS FOR MODELLING OF NON-LINEAR NON-STATIONARY PROCESSES

Recently introduced concept of evolving fuzzy modeling will be reviewed in this paper in the context of non-linear on-line modeling and identification of non-stationary processes. It will be presented as a logical consequence of the attempts to cope with the non-stationarity, non-linearity and the requirements for practical on-line algorithms that work in real-time and are close to the theoretically optimal/analytical solutions. The basic elements of the concept and its procedure will be outlined. This concept will be presented as a higher level adaptation that concerns model structure as well as model parameters. It will be presented as an extension of the multi-model concept and of the on-line identification of fixed structure fuzzy rule-based models. The name ‘evolving’ will be justified by the features of the algorithm such as ‘inheritance', ‘gradual change’, ‘learning by experience’, 'self-organization', and ‘age’ that are typical for the evolution of living individuals and especially humans. One should distinguish this from the ‘evolutionary computation’ algorithms also called ‘genetic’ algorithms where the evolution of population of individuals is mimicked and operations such as ‘crossover’, ‘mutation’, ‘reproduction’ and 'selection’ are used. From a pragmatic point of view, the new paradigm of ‘evolving systems’ is closer to on-line, real-time identification and adaptive systems branches of control theory while ‘evolutionary algorithms’ are heuristics which is firmly in the field of artificial intelligence. A number of application of this technique to a range of industrial and benchmark processes has been recently reported. Due to the lack of space only one problem related to building energy will be presented, namely modeling a typical air-conditioning unit will be presented.

523 フィードフォワード適応制御による片持梁の共振点での振動抑制手法の検討

One of the most serious problem we are facing is the number of estimated parameters is too big to be calculated by using a micro-computer. Moreover, genera feedforward adaptive control algorithm needs real-time system identification of the plant. In this study, we propose a simple method to attenuate only resonance peaks of vibration of cantilever beam based on MIT rule. The parameters to be estimated in real-time are only the amplitude and phase of a sinusoidal signal to cancel the vibration at resonant points of the beam. This simple algorithm never need previous system identification and reference signal as to be needed in general adaptive systems. Some experimental results show the efficiency of the algorithm.

Electron cyclotron emission temperature fluctuations associated with magnetic islands and real-time identification and control system

An important focus of the present research on thermonuclear fusion based on magnetic confinement is the control of rotating magnetic islands of low helical mode numbers m, n that are the nonlinear stage of resistive instabilities developing at magnetic surfaces where q = m/n. These perturbations are often driven by external resonant ‘error fields’ or mode coupling effects and maintained by local distortions of the current profile. The islands can be reduced in width or completely suppressed by a current driven by electron cyclotron waves accurately located within the island. A requisite for an effective control action is the ability to identify the relevant state variables (radial location, island amplitude, frequency and phase) in ‘real time’ and vary accordingly the control variables (wave beam power modulation and direction). Traditionally magnetohydrodynamic instabilities are monitored by external magnetic signals that however give no information about the radial location of the perturbation within the plasma. Here we describe the design of a real-time diagnostics/control device using an original algorithm for processing electron cyclotron emission signals to monitor temperature fluctuations and EC wave power deposition.

Multiple input design for real-time parameter estimation in the frequency domain

A method for designing multiple inputs for real-time dynamic system identification in the frequency domain was developed and demonstrated. The designed inputs are mutually orthogonal in both the time and frequency domains, with reduced peak factors to provide good information content for relatively small amplitude excursions. The inputs are designed for selected frequency ranges, and therefore do not require a priori models. The experiment design approach was applied to identify linear dynamic models for the F-15 ACTIVE aircraft, which has multiple control effectors.

Integrated fuzzy modeling and adaptive control for nonlinear systems

A systematic design methodology for integrating fuzzy modeling and adaptive control is proposed and developed in this paper. This design procedure provides a real-time system identification scheme using less fuzzy rules than that of the other existing methods due to a new sliding-mode learning mechanism embedded in the identified model, which has robust stability not only for stabilization of the identified system but also for trajectory tracking control. The integration of the identification and the adaptive control schemes ensures the suggested methodology overall advantageous and more attractive as compared to the other existing, usually separated, design approaches. Two typical complex systems are simulated, showing some convincing stabilization and tracking performance of the proposed integrated fuzzy system.

Experimental evaluation of adaptive predictive control for rotor vibration suppression

An on-line active control technique for suppressing rotor vibration is proposed. Linear voice coil motors mounted on a ball bearing housing are used for generating counter forces to cancel the transverse vibrations of shaft due to imbalance, misalignment, and so forth. Controllers are designed by using the impulse response-based model predictive control (IMPC) and generalized predictive control (GPC). Recursive-least-square (RLS) method is employed for real-time system identification. Multiple channel active control systems are implemented on the platform of a digital signal processor (DSP). Experimental evaluation indicated that the proposed methods were effective in suppressing the periodic disturbances due to constant as well as variable rotor speed. In particular, GPC has achieved the most satisfactory performance in terms of vibration attenuation and convergence speed.

Hidden Markov model-based real-time transient identifications in nuclear power plants

In this article, a transient identification method based on a stochastic approach with the hidden Markov model (HMM) has been suggested and evaluated experimentally for the classification of nine types of transients in nuclear power plants (NPPs). A transient is defined as when a plant proceeds to an abnormal state from a normal state. Identification of the types of transients during an early accident stage in NPPs is crucial for proper action selection. The transient can be identified by its unique time-dependent patterns related to the principal variables. The HMM, a double-stochastic process, can be applied to transient identification that is a spatial and temporal classification problem under a statistical pattern-recognition framework. The trained HMM is created for each transient from a set of training data by the maximum-likelihood estimation method which uses a forward-backward algorithm and the Baum-Welch re-estimation algorithm. The transient identification is determined by calculating which model has the highest probability for given test data using the Viterbi algorithm. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. There are also a few experimental tests that have been performed, including superimposing random noise, adding systematic error, and adding untrained transients to verify its performance and robustness. The proposed real-time transient identification system has been proven to have many advantages, although there are still some problems that should be solved before applying it to an operating NPP. Further efforts are being made to improve the system performance and robustness in order to demonstrate reliability and accuracy to the required level. © 2002 Wiley Periodicals, Inc.

Multi-Modal Vibration Control Using Adaptive Positive Position Feedback

An adaptive controller, Adaptive Positive Position Feedback (APPF) is proposed for the multi-modal vibration control of frequency varying structures. Spillover phenomena and real-time system identification have been obviously difficult obstacles for the multi-modal adaptive vibration control. To overcome these problems, a fast and powerful algorithm is proposed to identify the frequencies of time-varying structures. Variable PPF controllers are adjusted with estimated natural frequencies at every time step. A composite plate with a bonded piezoelectric sensor and an actuator was prepared as an experimental model, and the natural frequencies of the model are changed by attaching masses. The experimental results show that natural frequencies are estimated quite accurately and that the vibration of controlled modes is significantly reduced. No significant performance reduction has been observed with respect to approximately 10% frequency changes of the corresponding modes. On the contrary, the performance of the conventional LQG controller is significantly degraded due to frequency variations.

PID Controller of Knee Orthoses using Fuzzy Curve Fitting

In this paper, we discuss the mode ling and control of a pneumatic cylinder system for attaining high human-friendliness of active knee orthoses. First, we develop a linear modeling by applying first-order Taylor series expansion about the initial values of the controller. Second, we design a self-tuning PID controller by applying real-time system identification with fuzzy curve fitting. Finally, we conclude with some remarks on the future work directions