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.
|Number of pages||15|
|Journal||International Journal of Robust and Nonlinear Control|
|Publication status||Published - 2003|
- model predictive control
- computational efficiency
- hot strip mill
Kouvaritaks, B., Cannon, M., Grimble, M. J., & Bulut, B. (2003). Nonlinear predictive control of hot strip rolling mill. International Journal of Robust and Nonlinear Control, 13(3-4), 365-380. https://doi.org/10.1002/rnc.823