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

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

The application of the extended Kaman filter to the problem of mobile robot navigation in a known environment is presented. An algorithm for, model-based localization that relies on the concept of a geometric beacon, a naturally occurring environment feature that can be reliably observed in successive sensor measurements and can be accurately described in terms of a concise geometric parameterization, is developed. The algorithm is based on an extended Kalman filter that utilizes matches between observed geometric beacons and an a priori map of beacon locations. Two implementations of this navigation algorithm, both of which use sonar, are described. The first implementation uses a simple vehicle with point kinematics equipped with a single rotating sonar. The second implementation uses a 'Robuter' mobile robot and six static sonar transducers to provide localization information while the vehicle moves at typical speeds of 30 cm/s. >

Mobile robot localization by tracking geometric beacons

Intelligent control may be viewed as the ability of a controller to operate in multiple environments by recognizing which environment is currently in existence and servicing it appropriately. An important prerequisite for an intelligent controller is the ability to adapt rapidly to any unknown but constant operating environment. This paper presents a general methodology for such adaptive control using multiple models, switching, and tuning. The approach was first introduced by Narendra et al. (1992) for improving the transient response of adaptive systems in a stable fashion. This paper proposes different switching and tuning schemes for adaptive control which combine fixed and adaptive models in novel ways. The principal mathematical results are the proofs of stability when these different schemes are used in the context of model reference control of an unknown linear time-invariant system. A variety of simulation results are presented to demonstrate the efficacy of the proposed methods.

Adaptive control using multiple models

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can "switch" from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently-between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations.

Interacting multiple model methods in target tracking: a survey

Interacting Multiple Model Methods in Target Tracking: A Survey

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

The purpose of this paper is to examine several Kalman filter algorithms that can be used for state estimation with a multiple sensor system. In a synchronous data collection system, the statistically independent data blocks can be processed in parallel or sequentially, or similar data can be compressed before processing; in the linear case these three filter types are optimum and their results are identical. When measurements from each sensor are statistically independent, the data compression method is shown to be computationally most efficient, followed by the sequential processing; the parallel processing is least efficient.

Kalman filter algorithms for a multi-sensor system

The purpose of this paper is to summarize 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.

/pdf/the-stochastic-control-of-the-f-8c-aircraft-using-the-nu1v253p65.pdf

The stochastic control of the F-8C aircraft using the multiple model adaptive control (MMAC) method

In this paper, we have studied the nonlinear observability theory and its relationship with nonlinear estimation problems. We first presented a necessary and sufficient condition for local observability of nonlinear systems. Based on the sufficient condition for local observability, we developed a global observability condition for a class of nonlinear systems. We have found that the observability of a nonlinear system is necessary for the existence of an unbiased, bounded-variance state estimate. However, different from the linear counterpart, the converse is not true in general. A sufficient condition for asymptotic observability for nonlinear systems is given. This condition also assures the asymptotic convergence, in both the almost sure and the mean square sense, of the maximum likelihood estimate to the true state vector.

On observability and unbiased estimation of nonlinear systems

The generalized likelihood ratio technique, a scheme for detecting and identifying abrupt changes in dynamic systems, is described in detail. Attention is given to distinguishability, detectability, and sensitivity to modeling errors. This failure detection technique is applied to a two-dimensional model of the F-8, flying at Mach .6 at 20,000 ft. in cumulus clouds.

K. Dunn

Papers

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

Kalman filter algorithms for a multi-sensor system

The stochastic control of the F-8C aircraft using the multiple model adaptive control (MMAC) method

On observability and unbiased estimation of nonlinear systems

Status report on the generalized likelihood ratio failure detection technique, with application to the F-8 aircraft