Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.


Open-loop unstable feedback systems with double-sided inputs : an explicit demonstration of self-consistency

Leithead, W.E. and Ragnoli, E. and O'Reilly, J. (2005) Open-loop unstable feedback systems with double-sided inputs : an explicit demonstration of self-consistency. In: Proceedings of the 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. IEEE, pp. 5198-5203. ISBN 0-7803-9567-0

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


The standard formulation of linear shift-invariant feedback systems in the doubly infinite time axis setting lacks self-consistency with seemingly irreconcilable difficulties having been identified when the system is open-loop unstable. The available options for circumventing these difficulties for discrete-time SISO systems are highlighted and the manner in which they are exploited to obtain a self-consistent framework by reformulating the feedback systems in the space of distributions is clarified. In addition, it is explicitly demonstrated that causality and stability of a standard example are implied by the causality and stability of the equivalent system when reformulated in the self-consistent framework.