The authors formulate and solve two related control-oriented system identification problems for stable linear shift-invariant distributed parameter plants In each of these problems the assumed a priori information is minimal, consisting only of a lower bound on the relative stability of the plant, an upper bound on a certain gain associated with the plant, and an upper bound on the noise level The first of these problems involves identification of a point sample of the plant frequency response from a noisy, finite, output time series obtained in response to an applied sinusoidal input with frequency corresponding to the frequency point of interest This problem leads naturally to the second problem, which involves identification of the plant transfer function in H/sub infinity / from a finite number of noisy point samples of the plant frequency response Concrete plans for identification algorithms are provided for each of these two problems >

Control oriented system identification: a worst-case/deterministic approach in H/sub infinity /

http://courses.ece.ubc.ca/574/Hjalmarsson_Automatica_March05.pdf

From experiment design to closed-loop control

Identification and control—closed-loop issues

http://ieeexplore.ieee.org/iel4/9/2991/00090229.pdf

Control Oriented System Identification: A Worst-case /Deterministic

This paper aims at introducing the reader to the various issues that arise in the development of a coherent methodology for the development of robust control design on the basis of models identified from data. When a reduced complexity model is identified with the purpose of designing a robust controller, the model is just a vehicle for the computation of a controller. The design of the identification and of the controller must be seen as two parts of a joint design problem. The central message of this paper is to show that the global control performance criterion must determine the identification criterion. This leads to non standard identification criteria, which can be minimized by appropriate experimental set-ups.

Towards a Joint Design of Identification and Control

A criterion for system identification is developed that is consistent with the intended use of the fitted model for modern robust control synthesis. Specifically, a joint optimization problem is posed which simultaneously solves the plant model estimate and control design, so as to optimize robust performance over the set of plants consistent with a specified experimental data set. >

A criterion for joint optimization of identification and robust control

Automated on-orbit frequency domain identification for large space structures

In this paper, an algorithm is introduced to find a minimum phase transfer function of specified order whose magnitude "tightly" overbounds a specified nonparametric function of frequency. This method has direct application to transforming nonparametric uncertainty bounds (available from system identification experiments) into parametric representations required for modern robust control design software (i.e., a minimum-phase transfer function multiplied by a norm-bounded perturbation).

A Linear Programming Approach to Characterizing Norm Bounded Uncertainty from Experimental Data

Recent experiences in the field of flexible structure control in space have indicated a need for on-orbit system identification to support robust control redesign to avoid in-flight instabilities and maintain high spacecraft performance. This paper highlights an automated frequency domain system identification methodology recently developed to fulfill this need. The methodology is focused to support 1) the estimation of system quantities useful for robust control analysis and design, 2) experiment design tailored to performing system identification in a typically constrained on-orbit environment 3) the automation of operations to reduce "human in the loop" requirements. A basic overview of the methodology is presented first, followed by an experimental verification of the approach performed on the JPL/AFAL test-bed facility.

Frequency Domain Identification Experiment on a Large Flexible Structure

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability.

/pdf/autonomous-frequency-domain-identification-theory-and-3dh3xznbg6.pdf

Y. Yam

Papers

A criterion for joint optimization of identification and robust control

Automated on-orbit frequency domain identification for large space structures

A Linear Programming Approach to Characterizing Norm Bounded Uncertainty from Experimental Data

Frequency Domain Identification Experiment on a Large Flexible Structure

Autonomous frequency domain identification: Theory and experiment