Adaptive control of linear stochastic systems

Deshpande, Jayant G.; Upadhyay, Triveni N.; Lainiotis, Demetrios G.

doi:10.1016/0005-1098(73)90017-4

Cited by 91 publications

(14 citation statements)

References 12 publications

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“…This section describes how to use the adaptive estimation technique of Lainiotis and coworkers (Hilborn and Lainiotis, 1969;Lainiotis, 1971;Deshpande et al, 1973;Lainiotis, 1976) to choose the best model at any time, and to perform state estimation. This is done by calculating model probabilities, that is, p ( j ) is the probability of the jth model being a valid representation of the plant.…”

Section: Bayesian Estimatormentioning

confidence: 99%

Estimation of nonlinear systems using linear multiple models

et al. 1997

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Section: Bayesian Estimatormentioning

confidence: 99%

Estimation of nonlinear systems using linear multiple models

et al. 1997

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“…The following is the problem of control of an nth-order stochastic linear time-invariant uncertain system on finite time [6]. The system is specified by the following model: where xt ∈ R n is the state, ut ∈ R 1 is the input, and zt ∈ R 1 is the measured output; Aθ ∈ R nxn and bθ, cθ T ∈ R n ; mθ; t ∈ R n is an external deterministic input for a given θ.…”

Section: Statement Of the Problem: Control Of Uncertain Systemsmentioning

confidence: 99%

Bounds on the Miss of Multiple-Model-Based Terminal Guidance Laws

Rusnak

2015

Journal of Guidance, Control, and Dynamics

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In previous papers, bounds on the root-mean-square miss of a radar-guided missile for different types of target maneuvers and the terminal phase period have been presented. In this paper, bounds on the miss of multiple-modelbased terminal guidance laws are presented. Analytical formulas of bounds on the root-mean-square miss of a radarguided missile that implements multiple-model estimation and guidance scheme against a maneuvering target are presented. These formulas use a set of core parameters that affect the miss distance, thus, they can be used for synthesis and analysis of the performance of radar-guided tactical missiles with the multiple-model estimation and guidance. The bounds are derived subject to the assumption that the missile guidance law and the estimator are fully matched to the missile dynamics, target maneuver, and glint noise. The glint is the dominant noise source, the missile applies frequency agility, there is no missile acceleration limit and heading error, and there is no limitation on the computing resources.

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“…Willner [7] proposed the MMAC algorithm as discussed in this thesis and showed that it performed well in relation to both the Independently, Deshpande et al [12] arrived at the same algorithm as an ad hoc extension of the estimation/ identification algorithm of Magill [8].…”

Section: )mentioning

confidence: 99%

An analysis of the multiple model adaptive control algorithm

Greene

Willsky²

1980

1980 19th IEEE Conference on Decision and Control Including the Symposium on Adaptive Processes

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The Multiple Model Adaptive Control algorithm has been used in applications of advanced control technology.However, in these applications, many undesirable characteristics of the method, such as high amplitude limit cycles, have been uncovered. In this thesis the basic types of behavior exhibited by the method are explored. This is done through the simulation and analysis of the method as applied to a sample system structure.This structure has been carefully chosen to exhibit the major phenomena of interest while remaining amenable to detailed analysis.Two major types of results are presented. First of all, detailed conditions for the existance of each of the types of behavior are developed for the special system structure under consideration.Of possibly greater significance are the qualitative insights which result from extrapolating the detailed conclusions to problems of more general structure.It is believed that the qualitative understandings developed in this thesis can form the basis for the introduction of design modifications (two of which are suggested in this thesis) and the development of a systematic methodology for the design of adaptive control systems using the Multiple Model Adaptive Control algorithms.

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Adaptive control of linear stochastic systems

Cited by 91 publications

References 12 publications

Estimation of nonlinear systems using linear multiple models

Estimation of nonlinear systems using linear multiple models

Bounds on the Miss of Multiple-Model-Based Terminal Guidance Laws

An analysis of the multiple model adaptive control algorithm

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