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.


Nonlinear predictive control of hot strip rolling mill

Kouvaritaks, B. and Cannon, M. and Grimble, M.J. and Bulut, B. (2003) Nonlinear predictive control of hot strip rolling mill. International Journal of Robust and Nonlinear Control, 13 (3-4). pp. 365-380. ISSN 1049-8923

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


Linear Model Predictive Control (MPC) has been applied successfully to numerous industrial problems, but its various extensions to the nonlinear case have not enjoyed the same measure of success. One of the major obstacles in this development is the prohibitive online computation required to execute receding horizon minimization of the predicted cost. This paper combines recent linear techniques, which allow for significant reductions in online computational load, with Linear Difference Inclusion in order to apply MPC to a rolling mill problem described by a set of algebraic and differential/integral nonlinear equations, discretized to give a suitable time-varying uncertain linear model. Through successive optimization of an approximate cost derived by linearization about predicted trajectories, we obtain MPC laws with guaranteed stability and convergence to a (possibly local) minimum of the performance index predicted on the basis of the full nonlinear model dynamics. The efficacy of the approach is illustrated by means of simulation results presented at the end of the paper.