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 Feasibility Analysis of Moving Bank Multiple Model Adaptive Estimation and Control Algorithms written by Karl P. Hentz (1LT, USAF.) and published by . This book was released on 1984 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Feasibility Analysis of Moving Bank Multiple Model Adaptive Estimation and Control Algorithms written by and published by . This book was released on 1984 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This investigation examines the feasibility of a moving bank multiple model adaptive estimation/control algorithm. Sliding bank multiple model adaptive estimation differs from conventional multiple adaptive estimation in that a substantially reduced number of elemental filters is required for the sliding bank estimator (9 elemental filters vs. 100 for the system modeled in this thesis). The positions in parameter space that the reduced number of elemental filters occupy are dynamically re-declared: i.e., the sliding bank of filters is moved about the parameter space in search of the true parameter point. Critical to the performance of the sliding bank estimator is the decision method that governs movement of the bank of elemental filters. Because of this, a number of different decision algorithms are discussed and their respective performance compared. Three controller designs are also examined: a single changeable-gain, a single fixed-gain, and a sliding bank multiple model adaptive controller. States of a damped second order system, with uncertain parameters (damping ratio and undamped natural frequency) are estimated by the sliding bank estimator and then regulated to the quiescent state by the controller. Performance of the sliding bank estimator/controller is compared to a benchmark of a single Kalman filter/LQ controller that has (artificial) knowledge of the true parameter values. Comparisons are based upon Monte Carlo analysis results. Originator-supplied keywords include: Adaptive control systems, Adaptive filters, Multiple model adaptive estimation, and Multiple model adaptive control.

Download or read book Moving Bank Multiple Model Adaptive Algorithms Applied to Flexible Spacecraft Control written by Paul G. Filios and published by . This book was released on 1985 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Critical to the performance of the moving-bank multiple model adaptive estimator is the decision logic used to determine which elemental filters are implemented in the bank, and when to change this decision. The decision logics discussed focus on three situations: initial acquisition of the unknown parameter values through reducing bank discretization; tracking the unknown parameter values through bank movement; and reacquisition of the unknown parameters following a large jump change in their values through expanding bank discretization. Ambiguity function analysis is used to predict performance in these situations. The system to be controlled is a simplified model of a large scale space structure. Its equations of motion are developed and placed in state space form, the states being the positions and velocities of the rigid body mode and the second and fourth bending modes. The state space matrices describing the system are computed based on nominal values for all physical parameters with the exception of the mass density of the structure arms and their modulus of elasticity. These two parameters are allowed to vary in discrete steps establishing the parameter space. It is then attempted to control the states to the quiescent state, using moving-bank multiple model adaptive algorithms. The results indicate that, although the system under study did not have a great need for adaptive estimation and control the multiple model adaptive estimator performs essentially identically to a single filter artificially knowledgeable of the uncertain parameter values.

Download or read book Moving Bank Multiple Model Adaptive Estimation Applied to Flexible Spacestructure Control written by Drew A. Karnick and published by . This book was released on 1986 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant problem in estimation and control is the uncertainty of parameters in the mathematical model used in the design of controllers and/or estimators. These parameters may be unknown, varying slowly, or changing abruptly due to a failure in the physical system. These changes in parameters often necessitate the identification of parameters within the mathematical model and changing the mathematical model during a real-time control problem. This is often referred to as adaptive control and/or estimation. This thesis investigates methods of adaptive control implementing a moving-bank multiple model adaptive estimator. Keywords: algorithms; truss models; computerized simulation; adaptive control systems; adaptive filters; perturbations.

Download or read book Robustness of a Moving Bank Multiple Model Adaptive Controller for a Large Space Structure written by Michael R. Schore and published by . This book was released on 1989 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: The robustness of moving-bank multiple model adaptive estimation (MMAE) and control (MMAC) algorithms is analyzed in this thesis. The mismatch of a 24-state truth model and a 6-state filter model are evaluated on the basis of MMAE/MMAC performance. A model developed using finite element analysis is used to approximate a large space structure which has a large central hub with appendages radiating out from it. The mass of the hub is considered to be much larger than the mass of the flexible structure. The model is developed in physical coordinates and then transformed into modal coordinates. To obtain a reduced order filter model, the method of singular perturbations is used. The actual positions and velocities of various physical points on the structure are used in the evaluation of the MMAE/MMAC algorithm performance. The results of this study of model mismatching indicates that the MMAE provides accurate position and velocity estimates even in the face of a 6-state to 24-state model mismatch. When a non-adaptive filter is used with a mismatched parameter location, the performance suffers slightly. The use of an adaptive estimator does provide improved performance in the face of uncertain parameter location. Stable control was obtained with the use of MMAC. For the case of nonadaptive filter and mismatched parameter location, the control algorithm behaved in a possible destructive manner. By allowing the filter to adapt to the initial parameter location, the MMAC algorithm provided stable control of the structure, even in the face of large disturbances. (AW).

Download or read book Control and Dynamic Systems V31 Advances in Aerospace Systems Dynamics and Control Systems Part 1 of 3 written by C.T. Leonides and published by Elsevier. This book was released on 2012-12-02 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control and Dynamic Systems: Advances in Theory in Applications, Volume 31: Advances in Aerospace Systems Dynamics and Control Systems, Part 1 of 3 deals with significant advances in technologies which support the development of aerospace systems. It also presents several algorithms and computational techniques used in complex aerospace systems. The techniques discussed in this volume include: moving-bank multiple model adaptive estimation, algorithms for multitarget sensor tracking systems; algorithms in differential dynamic programming; optimal control of linear stochastic systems; and normalized predictive deconvulation. This book is an important reference for practitioners in the field who want a comprehensive source of techniques with significant applied implications.

Download or read book Multiple Model Approaches To Nonlinear Modelling And Control written by R Murray-Smith and published by CRC Press. This book was released on 2020-11-25 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions. Many component methods are described, as well as discussion of the strategies available for building a successful multiple model approach.

Download or read book A Robustness Analysis of Moving Bank Multiple Model Adaptive Estimation and Control of a Large Flexible Space Structure written by Daniel F Van Der Werken (Jr) and published by . This book was released on 1988 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: For this thesis, the truth model consists of 24 states, and the filter model consists of 6 states. The object is to determine whether or not the state mismatch between the filter and truth models seriously degrades or totally confounds the adaptation process. The system model used is a rotating two-bay flexible space structure, approximating a space structure that has a hub with extending appendages. The mass of the hub is greater than the mass of the appendage, and finite element analysis is used to develop the mathematical model. The system is developed in physical coordinates, transformed to modal coordinates, and the method of singular perturbations is used to provide a reduced filter model. The two dimensional parameter space consists of variations of the mass and stiffness matrices for the two-bay truss. Results indicate stable and accurate state estimation when the bank is initially centered on the true parameter. Stable control and accurate parameter estimation are not achieved. When the bank is initially centered at an incorrect parameter, stable and accurate state estimation is not demonstrated. These results indicate a total confounding of the adaptation process due to the unmodeled states, since accurate estimation and control can be generated when there are no such unmodeled states. Keywords: Kalman filters; Moving bank algorithms; Adaptive estimation; Multiple model adaptive control; Adaptive filters, Theses. (EDC).

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 Nonlinear Model Based Process Control written by Rıdvan Berber and published by Springer Science & Business Media. This book was released on 1998 with total page 916 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasingly competitive environment within which modern industry has to work means that processes have to be operated over a wider range of conditions in order to meet constantly changing performance targets. Add to this the fact that many industrial operations are nonlinear, and the need for on-line control algorithms for nonlinear processes becomes clear. Major progress has been booked in constrained model-based control and important issues of nonlinear process control have been solved. This text surveys the state-of-the-art in nonlinear model-based control technology, by writers who have actually created the scientific profile. A broad range of issues are covered in depth, from traditional nonlinear approaches to nonlinear model predictive control, from nonlinear process identification and state estimation to control-integrated design. Advances in the control of inverse response and unstable processes are presented. Comparisons with linear control are given, and case studies are used for illustration.

Download or read book Nonlinear Model Based Process Control written by R. Berber and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 883 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ASI on Nonlinear Model Based Process Control (August 10-20, 1997~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August 1994 in Antalya on Methods of Model Based Process Control in a more general context. In 1994, the contributions and discussions convincingly showed that industrial process control would increasingly rely on nonlinear model based control systems. Therefore, the idea for organizing this ASI was motivated by the success of the first one, the enthusiasm expressed by the scientific community for continuing contact, and the growing incentive for on-line control algorithms for nonlinear processes. This is due to tighter constraints and constantly changing performance objectives that now force the processes to be operated over a wider range of conditions compared to the past, and the fact that many of industrial operations are nonlinear in nature. The ASI intended to review in depth and in a global way the state-of-the-art in nonlinear model based control. The list of lecturers consisted of 12 eminent scientists leading the principal developments in the area, as well as industrial specialists experienced in the application of these techniques. Selected out of a large number of applications, there was a high quality, active audience composed of 59 students from 20 countries. Including family members accompanying the participants, the group formed a large body of92 persons. Out of the 71 participants, 11 were from industry.

Download or read book Technology for Large Space Systems written by and published by . This book was released on 1986 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Download or read book Large Space Structures Systems in the Space Station Era written by and published by . This book was released on 1991 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Stochastic Digital Control System Techniques written by and published by Academic Press. This book was released on 1996-05-16 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Series:"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory."-IEEE Group Correspondence"This book will help all those researchers who valiantly try to keep abreast of what is new in the theory and practice of optimal control."--Control

Download or read book Technology for Large Space Systems A Bibliography with Indexes supplement 20 written by United States. National Aeronautics and Space Administration. Scientific and Technical Information Division and published by . This book was released on 1989 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: