Download or read book Multiple Model Adaptive Estimation and Control Redistribution Performance on the VISTA F 16 During Partial Actuator Impairments Volume 2 written by and published by . This book was released on 1997 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple Model Adaptive Estimation with Control Reconfiguration (MMAE/CR) capability to estimate and compensate for partial actuator failures, or "impairments" is investigated using the high-fidelity, nonlinear, six-degree-of-freedom, VISTA F-16 simulation which currently resides on the Simulation Rapid-Prototyping Facility (SRF). After developing a model for inserting partial actuator impairments into the VISTA F-16 truth model, research begins with a battery of single actuator impairment tests. This stage of research explores the capability of the existing MMAE algorithm to estimate single, partial actuator impairments, and helps to define refinements and expansions needed in the MMAE algorithm for the second phase of research: the detection and estimation of dual, total and partial actuator impairments. It is seen from the first stage of research that, while MMAE is able to estimate partial impairments, there are refinements needed, such as "probability smoothing and quantization", to compensate for the quality of MMAE probability data and to provide a better, more stable estimate value to the Control Reconfiguration module. The Kalman filters and the dual, partial failure filter banks necessary for the detection of dual, partial actuator impairments are also defined as a result of the single impairment tests. Fifteen more banks of "partial first-failure" Kalman filters are added to the existing MMAE algorithm, as well as the "bank swapping" logic necessary to transition to them. Once the revised and expanded MMAE/CR algorithm is ready, research begins on dual combinations of total and partial actuator impairments. While results of these tests (for other than total impairments) are not as good as originally hoped or expected, the potential-for better performance is evident.
Download or read book Applied Science Technology Index written by and published by . This book was released on 1996 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Practical Implementation of Multiple Model Adaptive Estimation Using Neyman Pearson Based Hypothesis Testing and Spectral Estimation Tools written by Peter D. Hanlon and published by . This book was released on 1996-09-01 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study investigates and develops various modifications to the Multiple Model Adaptive Estimation (MMAE) algorithm. The standard MMAE uses a bank of Kalman filters, each based on a different model of the system. Each of the filters predict the system response, based on its system model, to a given input and form the residual difference between the prediction and sensor measurements of the system response. Model differences in the input matrix, output matrix, and state transition matrix, which respectively correspond to an actuator failure, sensor failure, and an incorrectly modeled flight condition for a flight control failure application, were investigated in this research. An alternative filter bank structure is developed that uses a linear transform on the residual from a single Kalman filter to produce the equivalent residuals of the other Kalman filters in the standard MMAE. A Neyman Pearson based hypothesis testing algorithm is developed that results in significant improvement in failure detection performance when compared to the standard hypothesis testing algorithm. Hypothesis testing using spectral estimation techniques is also developed which provides superior failure identification performance at extremely small input levels.
Download or read book Adaptive Estimation and Control written by Keigo Watanabe and published by . This book was released on 1991 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unifies the partitioned adaptive estimators for stochastic systems and applies them to other estimation and control problems. The techniques, not restricted to lumped-parameter systems with unknown constant parameters, serve as a starting point for more complicated problems.
Download or read book An Optimal Parameter Discretization Strategy for Multiple Model Adaptive Estimation and Control written by Stuart N. Sheldon and published by . This book was released on 1989 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method is proposed for designing multiple model adaptive estimators (MMAE) to provide combined state and parameter estimation in the presence of an uncertain parameter vector. It is assumed that the parameter varies over a continuous region and a finite number of constant-gain filters is available for the estimation. The estimator elemental filters are chosen by minimizing a cost functional representing the average state prediction error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. A second-order example is used to illustrate the increase in performance over previously accepted filter selection methods. By minimizing a cost functional representing the average parameter prediction error autocorrelation, a parameter estimator is designed which is different from the state estimator. The parameter estimator found with this method provides lower average mean square parameter estimation error than previously accepted design methods. An analogous method is proposed for designing multiple model adaptive controllers to provide stabilizing control in the presence of an uncertain parameter vector. A finite number of constant-gain controllers is used to regulate a system with a parameter vector that varies over a continuous region of the parameter space. The controller elemental filters are chosen by minimizing a cost functional representing the average regulation error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. Keywords: Numerical optimization, Adaptive control systems, Theses. (aw).
Download or read book Generalized Multiple Model Adaptive Estimation written by Badr N. Alsuwaidan and published by . This book was released on 2008 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: ?Pub Inc In this dissertation a generalized multiple-model adaptive estimator (GMMAE) is presented that can be used to estimate the unknown noise statistics in filter designs. The assumed unknowns in the adaptive estimator are the process noise covariance elements. Multiple parameter elements are used to drive multiple-model parallel filters for state estimation. The current approach focuses on estimating the process noise covariance by sequentially updating weights associated with parameter elements through the calculation of the likelihood function of the measurement-minusestimate residuals, which also incorporates correlations between various measurement times. For linear Gaussian measurement processes the likelihood function is easily determined. For nonlinear Gaussian measurement processes, it is assumed that the linearized output sufficiently captures the statistics of the likelihood function by making the small noise assumption. A proof is provided that shows the convergence properties of the generalized approach versus the traditional multiple-model adaptive estimator (MMAE). Simulation results, involving a two-dimensional target tracking problem ans GPS-based position estimation problem using an extended Kalman filter, indicate that the new approach is able to correctly estimate the noise statistics.
Download or read book New Variations of Multiple Model Adaptive Estimation for Improved Tracking and Identification written by Christopher K. Nebelecky and published by . This book was released on 2013 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple model adaptive estimation (MMAE) is a recursive algorithm that uses a bank of estimators, each purposefully dependent on a particular hypothesis, to determine an estimate of an uncertain system under consideration while simultaneously tracking the system state. The first generation of MMAE, introduced by Magill in 1965 considered the estimators to act independently and in parallel, determining state estimates conditional with each hypothesis. Through computation of a normalized mode-conditioned likelihood, the conditional probability that each hypothesis correctly models the system is computed. Since Magill's seminal work, many offshoots of MMAE have been developed. Modifications have been reported, but are typically on on an application specific basis which limits their versatility. In this dissertation, two variations of MMAE are considered. The first variation is based on an observed flaw which leads to degenerate tracking performance. The second variation is motivated by previous research which showed improved convergence performance by considering a generalized mode-conditioned likelihood function for determining the hypothesis conditional probabilities. Each estimator, or specifically Kalman filter, is designed around a particular system hypothesis. If the hypothesis is not sufficiently close to the true system, the resulting filter will generally produce erroneous estimates which do not track the system. This is because each filter believes that the hypothesized system is optimal. Further, the state error covariances resulting from such a suboptimal filter will be inconsistent because they have no knowledge of the incorrect hypothesized model. By explicitly accounting for the deviation of the hypothesis, recursions are developed which, when combined with MMAE are shown to provide superior tracking performance over the standard MMAE. Additionally the proposed variation, called model error MMAE, is shown to provide acceptable tracking performance for dynamically switching systems at a fraction of the computational expense of other algorithms specifically developed for that application. The second variation, referred to as generalized multiple model adaptive estimation (GMMAE), uses an augmented vector of current and past residuals to drive the recursion for the hypothesis conditional probabilities. Necessary for that recursion is evaluation of the time-domain autocovariance matrix of the residual sequence. When filtering linear (and linearized) systems, the autocovariance can be analytically expressed as a function of the system matrices, covariances and filter gain. When filtering nonlinear systems using the Unscented filter, analytic expressions for the autocovariance are not possible.^Motivated to include Unscented filters within the GMMAE framework, a method for calculating the time-domain autocovariance of the residual sequence from an Unscented filter is presented. The proposed method is validated analytically on a simplified system and simulation results are presented using the algorithm for process noise estimation in a planar tracking problem.
Download or read book Generalized Residual Multiple Model Adaptive Estimation of Parameters and States written by Charles D. Ormsby and published by . This book was released on 2003-10 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops a modification to the standard Multiple Model Adaptive Estimator (MMAE) which allows the use of a new "generalized residual" in the hypothesis conditional probability calculation. The generalized residual is a linear combination of traditional Kalman filter residuals and "post-fit" Kalman filter residuals which are calculated after measurement incorporation. This modified MMAE is termed a Generalized Residual Multiple Model Adaptive Estimator (GRMMAE). The dissertation provides a derivation of the hypothesis conditional probability formula which the GRMMAE uses to calculate probabilities that each elemental filter in the GRMMAE contains the correct parameter value. Through appropriate choice of a single scalar GRMMAE design parameter, the GRMMAE can be designed to be equivalent to a traditional MMAE, a post-fit residual modified MMAE, or any number of yet unused MMAEs. The original GRMMAE design goal was to choose the GRMMAE design parameter that caused the fastest GRMMAE convergence to the correct hypothesis. However, this dissertation demonstrates that the GRMMAE design parameter can lead to beta-dominance, a negative performance effect in the GRMMAE. This is a key contribution of this research as other researchers have previously suggested that the use of post-fit residuals may be advantageous in certain MMAE applications. This dissertation directly addresses the use of post-fit residuals by those researchers and demonstrates that, for their application, equivalent performance is achieved using a traditional MMAE. (10 tables, 27 figures, 47 refs.)
Download or read book Adaptive Estimation and Detection Techniques with Applications written by Jifeng Ru and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Hybrid systems have been identified as one of the main directions in control theory and attracted increasing attention in recent years due to their huge diversity of engineering applications. Multiple-model (MM) estimation is the state-of-the-art approach to many hybrid estimation problems. Existing MM methods with fixed structure usually perform well for problems that can be handled by a small set of models. However, their performance is limited when the required number of models to achieve a satisfactory accuracy is large due to time evolution of the true mode over a large continuous space. In this research, variable-structure multiple model (VSMM) estimation was investigated, further developed and evaluated. A fundamental solution for on-line adaptation of model sets was developed as well as several VSMM algorithms. These algorithms have been successfully applied to the fields of fault detection and identification as well as target tracking in this thesis. In particular, an integrated framework to detect, identify and estimate failures is developed based on the VSMM. It can handle sequential failures and multiple failures by sensors or actuators. Fault detection and target maneuver detection can be formulated as change-point detection problems in statistics. It is of great importance to have the quickest detection of such mode changes in a hybrid system. Traditional maneuver detectors based on simplistic models are not optimal and are computationally demanding due to the requirement of batch processing. In this presentation, a general sequential testing procedure is proposed for maneuver detection based on advanced sequential tests. It uses a likelihood marginalization technique to cope with the difficulty that the target accelerations are unknown. The approach essentially utilizes a priori information about the accelerations in typical tracking engagements and thus allows improved detection performance. The proposed approach is applicable to change-point detection problems under similar formulation, such as fault detection.
Download or read book Multiple Model Adaptive Estimation Applied to the LAMBDA URV for Failure Detection and Identification written by David W. Lane (CAPT, USAF.) and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book New Algorithms for Moving Bank Multiple Model Adaptive Estimation written by Juan R. Vasquez and published by . This book was released on 1998-05-01 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard Multiple Model Adaptive Estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. A new moving-bank MMAE algorithm is developed based on exploitation of the density data available from the MMAE. The methods used to exploit this information include various measures of the density data and a decision-making logic used to move, expand, and contract the MMAE bank of filters. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters. A new parameter discretization method is developed based on the probabilities associated with the generalized Chi-Squared random variables formed by residual information from the elemental Kalman filters within the MMAE. Modifications to an existing discretization method are also presented, permitting application of this method in real time and to nonlinear system models or linear/linearized models that are unstable or astable. These new algorithms are validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.
Download or read book Beyond the Kalman Filter Particle Filters for Tracking Applications written by Branko Ristic and published by Artech House. This book was released on 2003-12-01 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
Download or read book Stochastic Models Estimation and Control written by Peter S. Maybeck and published by Academic Press. This book was released on 1982-08-25 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume builds upon the foundations set in Volumes 1 and 2. Chapter 13 introduces the basic concepts of stochastic control and dynamic programming as the fundamental means of synthesizing optimal stochastic control laws.
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1991 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On the Frontier written by Richard P. Hallion and published by . This book was released on 1984 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Microphone Handbook written by John Eargle and published by Elar Publishing Company, Incorporated. This book was released on 1982 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: OUVRAGE SUR LES DIFFERENTS ASPECTS DE L'UTILISATION DU MICROPHONE.
