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This paper uses an input-output stability analysis approach to show that for a large class of output error identification algorithms, the usual strict positive real (SPR) conditions on the unknown plant can be replaced by "persistent power" conditions on the plant input sequence. The only a priori knowlege of the plant assumed is stability and knowlege of a model order upper bound. This class of algorithms is shown to include the constant direction, recursive least squares with forgetting, controlled trace, and covariance resetting variants, extending the results of [1]. Arguments for the necessity of the SPR condition in other cases, eg. recursive least squares and stochastic approximation, are also given. Implications in identification and adaptive IIR filtering are discussed.

This paper reports some preliminary results concerning robustness properties of adaptive control systems to unmodeled dynamics and bounded disturbances. The analysis is conducted from the viewpoint of input/output stability theory. Generic representations are proposed for both continuous-time and discrete-time adaptive systems and conditions for stability and robustness are developed for each case. These conditions require varying degrees of a priori knowledge about the plant, e.g., global conditions involving minimal knowledge and local conditions involving more restrictive assumptions.

An adjustable model reference adaptive control strategy is proposed where both the controller and reference model parameters are adapted. Adaptive control laws for this structure implementing pole cancellation and replacement and avoiding the zero cancellation usually required by model reference adaptive control are developed from self-tuning adaptive algorithm modification.

Recently, we proposed a recursive maximum likelihood (RML) algorithm for the purpose of adaptive differential pulse code modulation (ADPCM). In this paper, the effects on the performance of this algorithm due to the quantization of the prediction error are studied.

Indirect adaptive pole placers combining plant parameter estimation and algebraic solution of the pole placement problem require, at least, a minimal convergent plant parameterization to avoid requesting infinite controller gains. This is impossible if the plant order is overspecified. A new direct adaptive pole placer, based on the inclusion by Elliott of the Bezout identity in the prediction error, is shown in this paper to, at least sometimes, bypass this restriction.

This short paper is intended as an introduction to the invited session by the same name held at the 21st Conference on Decision and Control at Orlando, Florida in December 1982. This paper provides a preliminary categorization of robustness issues in adaptive control and introduces the six papers to be presented by relating them to this framework.

This paper elucidates the common parameter estimation basis of adaptive filtering, identification, and control. This is achieved by converting the direct input-output form of the discrete adaptive infinite impulse response filtering, adaptive output error identification, and model reference adaptive control problems to a single state-variable form stable error model. This interconnection encourages further cross-fertilization of problems and solutions among these frequently disjointly pursued applications of adaptive parameter estimation.

The dual-momentum control device being studied for large spacecraft consists of two counter-rotating rings, each designated as an annular momentum control device (AMCD). For large rings, flexibility is appreciable and it becomes necessary to account for the distributed nature of the rings in the design of the magnetic bearing controllers. Also, ring behavior is unpredictably sensitive to ring temperature, spin rate, manufacturing imperfections, and other variables. For that reason, an adaptive control system is being sought for ring stabilization and maneuvering. This paper details an original adaptive control methodology for distributed parameter systems and illustrates this technique by application to AMCD stabilization.

We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems. In this paper, we discuss how to update the solution as well as input parameters involved in the simulation based on local measurements. The updates are performed in time. We test our method on various synthetic examples.

In this issue of CG&A, researchers share their R&D findings and results on applying visual analytics (VA) to extreme-scale data. Having surveyed these articles and other R&D in this field, we’ve identified what we consider the top challenges of extreme-scale VA. To cater to the magazine’s diverse readership, our discussion evaluates challenges in all areas of the field, including algorithms, hardware, software, engineering, and social issues.

This paper provides further study of a recently suggested algorithm for the adaption of an IIR filter used as a sequentially adaptive backward predictor within an adaptive differential pulse code modulation (ADPCM) scheme.

General expressions for the moments of the binomial and Pascal distributions were found. These expressions permit convenient moment evaluation utilizing a moment coefficient triangle. Although the expression for the moments of a binomial distribution was derived for the order of the moment less than or equal to the number of trials, (Le., m < n), it can be easily shown that the general form of (3), valid for all positive moments, is