On-line system identification for control system applications in particle accelerators

Particle accelerators require a number of feedback systems in order to stabilize a variety of parameters. The Continuous Electron Beam Accelerator at Thomas Jefferson National Accelerator Facility presents a unique set of control and identification problems. This accelerator produces a continuous electron beam with energies between 0.5 and 4.0 GeV to be delivered to the experimental halls. In order to meet stringent beam quality requirements specified by the experimental halls, the position and the energy of the electron beam needs to stabilized at various locations in the accelerator.;A number of noise measurement tests were conducted at various locations in the accelerator to obtain accurate information about the amplitude and the frequency of disturbances on the beam orbit and energy. Results of these measurements indicate that the line power harmonics were the primary source of disturbance on the beam orbit and energy.;A prototype fast feedback system was implemented in the injector and the East Arc regions of the accelerator to stabilize the beam position and energy at these locations. The scheme of implementation of these systems and measurements of their performance are presented here.;These feedback systems have to operate under conditions of varying noise characteristics and changing dynamics of the systems. For the feedback systems to always perform optimally, the knowledge of time varying noise characteristics and changing system dynamics needs to be incorporated into the feedback strategy. The approach presented in this work is to perform on-line system identification using a formulation of Fast Transversal Filter (FTF) in order to extract the time varying information from input/output data of the feedback system.;A simulation test stand was developed using an analog computer to represent a continuous time system whose noise characteristics and dynamics could be changed in a controlled manner. An on-line system identification algorithm was implemented on a microprocessor similar to the ones used in the accelerator control system. Experience with the hardware-in-loop simulation for various cases of changing system dynamics and noise characteristics and the performance results of the on-line system identification algorithm operating under these conditions are presented in this dissertation

On issues of equalization with the decorrelation algorithm : fast converging structures and finite-precision

To increase the rate of convergence of the blind, adaptive, decision feedback equalizer based on the decorrelation criterion, structures have been proposed which dramatically increase the complexity of the equalizer. The complexity of an algorithm has a direct bearing on the cost of implementing the algorithm in either hardware or software. In this thesis, more computationally efficient structures, based on the fast transversal filter and lattice algorithms, are proposed for the decorrelation algorithm which maintain the high rate of convergence of the more complex algorithms. Furthermore, the performance of the decorrelation algorithm in a finite-precision environment will be studied and compared to the widely used LMS algorithm

LMS Adaptive Filters for Noise Cancellation: A Review

This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted

Channel estimators for HF radio links

The thesis is concerned with the estimation of the sampled impulse-response (SIR), of a time-varying HF channel, where the estimators are used in the receiver of a 4800 bits/s, quaternary phase shift keyed (QPSK) system, operating at 2400 bauds with an 1800 Hz carrier. T= FIF modems employing maximum-likelihood detectors at the receiver require accurate knowledge of the SIR of the channel. With this objective in view, the thesis considers a number of channel estimation techniques, using an idealised model of the data transmission system. The thesis briefly describes the ionospheric propagation medium and the factors affecting the data transmission over BF radio. It then presents an equivalent baseband model of the I-IF channel, that has three separate Rayleigh fading paths (sky waves), with a 2Hz frequency spread and transmission delays of 0,1.1 and 3 milliseconds relative to the first sky wave. Estimation techniques studied are, the Gradient estimator, the Recursive leastsquares (RLS) Kalman estimator, the Adaptive channel estimators, the Efficient channel estimator ( that takes into account prior knowledge of the number of fading paths in the channel ), and the Fast Transversal Filter (F-FF), estimator (which is a simplified form of the Kalman estimator). Several new algorithms based on the above mentioned estimation techniques are also proposed. Results of the computer simulation tests on the performance of the estimators, over a typical worst channel, are then presented. The estimators are reasonably optimized to achieve the minimum mean-square estimation error and adequate allowance has been made for stabilization before the commencement of actual measurements. The results, therefore, represent the steady-state performance of the estimators. The most significant result, obtained in this study, is the performance of the Adaptive estimator. When the characteristics of the channel are known, the Efficient estimators have the best performance and the Gradient estimators the poorest. Kalman estimators are the most complex and Gradient estimators are the simplest. Kalman estimators have a performance rather similar to that of Gradient estimators. In terms of both performance and complexity, the Adaptive estimator lies between the Kalman and Efficient estimators. FTF estimators are known to exhibit numerical instability, for which an effective stabilization technique is proposed. Simulation tests have shown that the mean squared estimation error is an adequate measurement for comparison of the performance of the estimators

The use of a fuzzy logic approach for integration of vibration-based diagnostic features of rolling element bearings

Modern condition monitoring systems (CMS) collect and process enormous amount of data in order to provide the earliest and most dependable information of fault development within any of the machine components and their operation combined. According to numerous studies one of the most fault susceptible mechanical elements in rotating machinery are rolling element bearings. Although reliable techniques for their diagnostics are already proposed, the new investigation is needed. According to authors experience in many industrial applications the operators are obligated to simultaneously track hundreds of diagnostic estimates, such as signals energy, its peakedness or narrowband characteristics for localized faults. As mentioned, for a vibration-based CMS of single wind turbine there are nearly 150 of them. Therefore, the authors employ a fuzzy logic approach for integration of bearing diagnostic features. A new estimate that carry most relevant information about bearing condition is discussed. The reasoning is presented on simulated data that mimics real rotating machine