Showing papers by "John B. Moore published in 1987"
TL;DR: In this article, the authors generalized the loop recovery technique for non-minimum-phase plants in the following sense: the open-loop properties of certain partial state feedback designs are recovered in a state estimation feedback controller design involving the addition of fictitious plant noise.
Abstract: The loop recovery technique has been generalized for nonminimum-phase plants in the following sense. The open-loop properties of certain partial state feedback designs are recovered in a state estimation feedback controller design involving the addition of fictitious plant noise. The partial state is the state of a minimum-phase factor in a minimum-phase, all-pass factored form model. Of course, robust designs are expected only when these are achieved for the case of partial state feedback of only the minimum-phase factor states. This may not always be possible. For the case of minimum-phase plants, known designs and theory are recovered as a special case. The theory and designs of this note generalize to include frequency shaping of both the control objectives and the loop recovery.
45 citations
TL;DR: In this article, a generalized frequency-shaped LQ theory is developed for plants with matrix fraction descriptions, and a spectral factorization based construction procedure is given which leads to stable output feedback controllers that are optimal in an LQ sense.
33 citations
TL;DR: In this article, discrete-time convergence rates for extended least squares (ELS) algorithms are generalized to the continuous-time case, and an essential difference in the estimation is the appropriate prefiltering, while in the theory the existence of solutions of the stochastic equations is a concern.
Abstract: Discrete-time convergence rates for extended least squares (ELS) algorithms are generalized to the continuous-time case. An essential difference in the estimation is the appropriate prefiltering, while in the theory, the existence of solutions of the stochastic equations is a concern.
24 citations
TL;DR: In this article, a continuous-time adaptive estimation scheme associated with a class of finite dimensional, time invariant, linear stochastic signal models is presented, and a global convergence theory is given for such schemes under a coloured noise/prefiller positive real condition.
19 citations
Patent•
19 Nov 1987
TL;DR: In this article, an output vector produced by a plant in response to an input vector is filtered by a plurality of bandpass filters, each filter having a different pass band, and a filtered output vector from each filter is provided as input to a separate adaptive feedback controller, and feedback vectors produced by the separate controllers are summed to provide the input vector to the plant.
Abstract: An output vector produced by a plant in response to an input vector is filtered by a plurality of bandpass filters, each filter having a different pass band. A filtered output vector from each filter is provided as input to a separate adaptive feedback controller, and feedback vectors produced by the separate controllers are summed to provide the input vector to the plant. Each adaptive feedback controller continuously identifies an open-loop transfer function of the plant characterizing a particular frequency band of plant operation, and continuously adjusts its own open-loop transfer function so that the poles of the closed-loop transfer function of the plant for that particular frequency mode of operation are assigned to constant stable values despite changes in the open-loop transfer function of the plant.
17 citations
TL;DR: In this paper, it is shown that there is a universality advantage for any externally applied signal to be stochastic rather than deterministic, and that when such unpredictable signals excite an adaptive control scheme, there is no need to deliberately constrain the adaptation to be'slow' or 'excitation maintaining' to ensure adequate identification.
16 citations
01 Nov 1987
TL;DR: The methods proposed in the paper are relatively simple compared to on-line order determination, being based on introducing suitable excitation in the "regression" vectors of the parameter estimation algorithms to ensure parameter convergence.
Abstract: In this paper, a first step is taken to avoid ill-conditioning in adaptive estimation and pole assignment schemes for the case when there is a signal model overparametrization. Such a situation can occur in practice when an unknown model order is guessed too high so as to be on the "safe" side. The methods proposed in the paper are relatively simple compared to on-line order determination, being based on introducing suitable excitation in the "regression" vectors of the parameter estimation algorithms to ensure parameter convergence. For the case when the models are nonunique in that pole-zero cancellations can occur,the algorithms seek to estimate the unique model where the cancellations occur at the origin. Applying estimates of this (unique) model turns out to avoid ill-conditioning in central tendency adaptive pole assignment. For the case of one pole-zero cancellation the convergence theory of the algorithm is complete. For the more general case, algorithms are readily devised which appear to work well but for which a complete theory is not available.
11 citations
TL;DR: Classes of stabilizing decentralized controllers for linear systems with plant state estimate feedback at each control station are studied.
4 citations
TL;DR: The theory of the paper makes a connection between the least squares parameter error equations and those associated with extended least squares using a posteriori noise estimates for the case of adaptive minimum variance control of minimum phase plants, which permits stronger convergence results than those hitherto derived from the theory of extended least square based on a priori noise Estimates.
2 citations