Showing papers by "John B. Moore published in 1988"

Left coprime factorizations and a class of stabilizing controllers for nonlinear systems

Abstract: The problem of representing a class of nonlinear systems by both left and right BIBO (bounded-input-bounded-output) coprime factors is studied. Based on the coprime factorizations, the class of all stabilizing controllers of a particular structure for the set of plants under consideration is then characterized in terms of a BIBO stable map, Q. The results specialize to the Youla-Kucera parameterization in the linear case. Results giving the various conditions for a nonlinear feedback system to be well-posed are also presented. >

Tracking randomly varying parameters-analysis of a standard algorithm

Abstract: Concerns the use of the Kalman filter as an algorithm for the parameter estimation of a linear stochastic system where the unknown parameters are randomly time-varying and can be represented by a Markov model. The authors develop asymptotic properties of the algorithm. In particular they establish the tracking error bounds for the unknown parameters. It is shown that the Kalman filter has quite reasonable tracking properties even in the non-Gaussian case when it is not an optimal filter. If the parameters are generated from a stable model, it is found that there is no restriction on the regressors to achieve tracking error bounds. The bounds obtained have application for adaptive controller analysis. >

Robust recursive identification of multidimensional linear regression models

Abstract: Stochastic adaptive estimation and control algorithms involving recursive prediction estimates have guaranteed convergence rates when the noise is not ‘too’ coloured, as when a positive-real condition on the noise mode is satisfied. Moreover, the whiter the noise environment the more robust are the algorithms. This paper shows that for linear regression signal models, the suitable introduction of while noise into the estimation algorithm can make it more robust without compromising on convergence rates. Indeed, there are guaranteed attractive convergence rates independent of the process noise colour. No positive-real condition is imposed on the noise model.

Controller reduction methods maintaining performance and robustness

Abstract: Standard balanced-truncation or Hankel-norm model approximation methods are applied to augmentations of the controller which emerge when characterizing the class of all stabilizing controllers in terms of an arbitrary proper stable transfer function. In the method, scaling parameters are at the disposal of the engineer to achieve an appropriate compromise between preserving performance for the nominal plant and a certain type of robustness of plant variations. There are a number of unique features of the approach. One feature is that a straightforward reoptimization of a reduced-order controller is possible within the framework of the method. A second feature is that for controllers designed for simultaneous stabilization of a number of plants, the method seeks to preserve the performance/robustness of the reduced-order controller for each plant. >

On the Class of All Stabilizing Regulators

Abstract: The `internal model principle' for linear multivariable regulators tells us necessary and sufficient conditions for achieving stabilizing controllers which achieve structurally stable output regulation for classes of deterministic exogenous reference and disturbance signals. In the absence of structural stability considerations, Antoulas has characterized the entire class of stabilizing regulators in the time-invariant case, building on the Kuc?era/Youla parametrization techniques. This paper gives an alternative characterization of the class of stabilizing regulators which is convenient to achieve on-line or off-line H2/H? optimization for performance and robustness. The characterization exploits distubance signal reconstruction, and nulling filters. The theory allows a re-interpretation of the necessary and sufficient conditions for regulation. Also novel connections between structurally stable regulation and the internal model principle are exposed.