Picture of virus under microscope

Research under the microscope...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

Explore SIPBS research

Restricted structure control of multiple model systems with series 2 DOF tracking and feedforward action

Grimble, M.J. (2001) Restricted structure control of multiple model systems with series 2 DOF tracking and feedforward action. Optimal Control Applications and Methods, 22 (4). pp. 157-196. ISSN 0143-2087

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

The solution of a scalar optimal control problem is discussed where the feedback, series tracking and feedforward controllers are chosen to have a very simple. Each controller term may be chosen to be of reduced order, lead/lag, or PID forms, and the controller is required to minimize an LQG cost-index. The optimization is based upon a cost-function which also allows separate costing of the terms due to the feedback, tracking and feedforward controllers. The system model can be uncertain and can be represented by a set of models over which the optimization is performed. This provides a form of robust optimal control that might even be applied to non-linear systems that can be approximated by a set of linearized models. The theoretical problem considered is to obtain the causal, stabilizing, feedback, series-tracking and feedforward controllers, of a prespecified form, that minimize an LQG criterion over the set of possible linear plant models. The underlying practical problem of importance is to obtain a simple method of tuning low-order controllers, given only an approximate model of the process. The results are illustrated in a power generation control problem for a system represented by 12 different linearized plant models. The single feedback controller that is obtained has a simple form and stabilizes the full set of models.