https://ntrs.nasa.gov/api/citations/19760004259/downloads/19760004259.pdf

Paper: A survey of design methods for failure detection in dynamic systems

We consider a class of stochastic linear systems that are subject to jumps of unknown magnitudes in the state variables occurring at unknown times. This model can be used when considering such problems as estimation for systems subject to possible component failures and the tracking of vehicles capable of abrupt maneuvers. Using Kalman-Bucy filtering and generalized likelihood ratio techniques, we devise an adaptive filtering system for the detection and estimation of the jumps. An example that illustrates the dynamical properties of our filtering scheme is discusssed in detail.

A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems

The purpose of this paper is to summarize some results obtained for the adaptive control of the F-8C aircraft using the so-called MMAC method. The discussion includes the selection of the performance criteria for both the lateral and the longitudinal dynamics, the design of the Kalman filters for different flight conditions, the "identification" aspects of the design using hypothesis testing ideas, and the performance of the closed-loop adaptive system.

The stochastic control of the F-8C aircraft using a multiple model adaptive control (MMAC) method--Part I: Equilibrium flight

A signal analysis technique is developed for discriminating a set of lower arm and wrist functions using surface EMG signals. Data wete obtained from four electrodes placed around the proximal forearm. The functions analyzed included wrist flexion/extension, wrist abduction/adduction, and forearm pronation/supination. Multivariate autoregression models were derived for each function; discrimination was performed using a multiple-model hypothesis detection technique. This approach extends the work of Graupe and Cline [1] by including spatial correlations and by using a more generalized detection philosophy, based on analysis of the time history of all limb function probabilities. These probabilities are the sufficient statistics for the problem if the EMG data are stationary Gauss-Markov processes. Experimental results on-normal subjects are presented which demonstrate the advantages of using the spatial and time correlation of the signals. This technique should be useful in generating control signals for prosthetic devices.

Upper Extremity Limb Function Discrimination Using EMG Signal Analysis

There is a vast literature about adaptive (self-organizing, self-optimizing) control systems. The aim of this paper is to point out some of the main ideas. Since there exists good survey papers about several of the classes of adaptive controllers the main part of this paper will concentrate on recent developments. The paper will cover stochastic adaptive controllers. The problem of dual control is given particular attention.

Stochastic adaptive control methods: a survey

A flight control system design is presented, that can be implemented by analog hardware, to be used to control an aircraft with uncertain parameters. The design is based upon the use of modern control theory. The ideas are illustrated by considering control of STOL longitudinal dynamics.

A Practical Scheme for Adaptive Aircraft Flight Control Systems

: The purpose of this report is to examine the properties of Kalman-Bucy filters, especially in the case of mismatching, i.e., when the nominal parameters used in the realization of the Kalman-Bucy filter are different from the actual plant parameters. In such cases, the mismatched filters give rise to bias and RMS errors. By making tests upon signals that appear in the filter, e.g., the residuals, one can detect the existence of mismatching. Hence, by knowing the properties of both matched and mismatched filters, one can then attempt to correct the estimates of the mismatched filters by on-line signal processing. (Author)

Dieter Willner

Papers

A Practical Scheme for Adaptive Aircraft Flight Control Systems

Properties of Matched and Mismatched Kalman Filters.