Presents an approach to the solution of the output feedback robust control problem. The authors employ the concept of information state for output feedback dynamic games, and obtain necessary and sufficient conditions...
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Presents an approach to the solution of the output feedback robust control problem. The authors employ the concept of information state for output feedback dynamic games, and obtain necessary and sufficient conditions for the solution to the robust control problem expressed in terms of the information state. The resulting controller is an information state feedback controller, and is intrinsically infinite dimensional.< >
Channel errors significantly reduce the performance of adaptive dequantizers. In this paper, we extend the techniques used in Kaiman filter based adaptive quantization to arrive at new dequantization schemes which are...
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Channel errors significantly reduce the performance of adaptive dequantizers. In this paper, we extend the techniques used in Kaiman filter based adaptive quantization to arrive at new dequantization schemes which are more robust to channel errors. This is achieved by utilising the estimates produced by a Kalman filter based on a linear signal model which embodies the entire encoder/channel combination. Extensions to Kaiman smoothing based on the same signal model result in further performance improvement at the expense of a small delay.
Many practical applications of control system design based on input-output measurements permit the repeated application of a system identification procedure operating on closed loop data together with successive refin...
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Many practical applications of control system design based on input-output measurements permit the repeated application of a system identification procedure operating on closed loop data together with successive refinement of the designed controller. Recently several iterative schemes for mutually enhanced plant identification and robust control design have been proposed [l]-[3]. In this paper we shall analyze the methodology of [1] from the viewpoint of closed loop signal conditioning and investigate the effect of the noise modelling error and plant modelling error on the closed loop performance.
The authors propose numerical techniques for parameter estimation of fast-sampled homogeneous Markov chains observed in white Gaussian noise. Continuous-time filters that estimate the quantities used in the expectatio...
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The authors propose numerical techniques for parameter estimation of fast-sampled homogeneous Markov chains observed in white Gaussian noise. Continuous-time filters that estimate the quantities used in the expectation-maximization (EM) algorithm for maximum likelihood parameter estimation have been obtained by R.J. Elliott (1991, 1992). The numerical work is based on the robust discretization of these filters. The advantage of using filters in the EM algorithm is that they have negligible memory requirements, independent of the number of observations. In comparison, standard discrete-time EM algorithms (Baum-Welch re-estimation equations) are based on smoothers and require the use of the forward-backward algorithm, which is a fixed-interval algorithm and has memory requirements proportional to the number of observations. Although the computational complexity of the filters at each time instant is O(N/sup 4/) (for a N state Markov) compared to O(N/sup 2/) for the forward-backward scheme, the filters are suitable for parallel implementation. Simulations are presented to illustrate the satisfactory performance of the algorithms.< >
This work is aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control sys tems, signal processing, and linear algebra. The motivation for th...
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ISBN:
(数字)9781447134671
ISBN:
(纸本)9781447134695
This work is aimed at mathematics and engineering graduate students and researchers in the areas of optimization, dynamical systems, control sys tems, signal processing, and linear algebra. The motivation for the results developed here arises from advanced engineering applications and the emer gence of highly parallel computing machines for tackling such applications. The problems solved are those of linear algebra and linear systems the ory, and include such topics as diagonalizing a symmetric matrix, singular value decomposition, balanced realizations, linear programming, sensitivity minimization, and eigenvalue assignment by feedback control. The tools are those, not only of linear algebra and systems theory, but also of differential geometry. The problems are solved via dynamical sys tems implementation, either in continuous time or discrete time , which is ideally suited to distributed parallel processing. The problems tackled are indirectly or directly concerned with dynamical systems themselves, so there is feedback in that dynamical systems are used to understand and optimize dynamical systems. One key to the new research results has been the recent discovery of rather deep existence and uniqueness results for the solution of certain matrix least squares optimization problems in geomet ric invariant theory. These problems, as well as many other optimization problems arising in linear algebra and systems theory, do not always admit solutions which can be found by algebraic methods.
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