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System Order (system + order)

Distribution by Scientific Domains

Engineering	80%

Selected Abstracts

Application of adaptive lattice filters to internal model control

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2008
G. Nikolakopoulos
Abstract An adaptive internal model control (IMC) framework is proposed in this article for infinite impulse response systems. The innovation in this study stems from the relaxed assumption that the controller needs to know a priori the system order. To bypass this restriction, a lattice filter identifies the system's order as well as its reflection coefficients. Within the IMC structure, a lattice-based controller is utilized in the forward path in cascade with a low-pass detuning filter. The controller self-configures its structure according to the estimated system order, while the filter's bandwidth increases as the identifier estimates more accurately the system dynamics. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Numerical solution of large-scale Lyapunov equations, Riccati equations, and linear-quadratic optimal control problems

NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, Issue 9 2008
Peter Benner
Abstract We study large-scale, continuous-time linear time-invariant control systems with a sparse or structured state matrix and a relatively small number of inputs and outputs. The main contributions of this paper are numerical algorithms for the solution of large algebraic Lyapunov and Riccati equations and linear-quadratic optimal control problems, which arise from such systems. First, we review an alternating direction implicit iteration-based method to compute approximate low-rank Cholesky factors of the solution matrix of large-scale Lyapunov equations, and we propose a refined version of this algorithm. Second, a combination of this method with a variant of Newton's method (in this context also called Kleinman iteration) results in an algorithm for the solution of large-scale Riccati equations. Third, we describe an implicit version of this algorithm for the solution of linear-quadratic optimal control problems, which computes the feedback directly without solving the underlying algebraic Riccati equation explicitly. Our algorithms are efficient with respect to both memory and computation. In particular, they can be applied to problems of very large scale, where square, dense matrices of the system order cannot be stored in the computer memory. We study the performance of our algorithms in numerical experiments. Copyright © 2008 John Wiley & Sons, Ltd. [source]

On ,, model reduction for discrete-time linear time-invariant systems using linear matrix inequalities,

ASIAN JOURNAL OF CONTROL, Issue 3 2008
Yoshio Ebihara
Abstract In this paper, we address the ,, model reduction problem for linear time-invariant discrete-time systems. We revisit this problem by means of linear matrix inequality (LMI) approaches and first show a concise proof for the well-known lower bounds on the approximation error, which is given in terms of the Hankel singular values of the system to be reduced. In addition, when we reduce the system order by the multiplicity of the smallest Hankel singular value, we show that the ,, optimal reduced-order model can readily be constructed via LMI optimization. These results can be regarded as complete counterparts of those recently obtained in the continuous-time system setting. [source]