Controller adjustment for improved nominal performance and robustness—II. Robust geometric control of a distillation column

Adaptive Linearizing Control with Neural-Network-Based Hybrid Models

“…It is normally difficult to obtain exact models, and most of the models only represent an approximate description of the plant. Kummel and Anderson have shown that the output sensitivity of geometric control toward modeling errors equals that of an open-loop controller. Other researchers have shown the dependence of these methods with respect to disturbance handling and modeling errors and suggest that these linearization techniques can be very sensitive to these factors. , They are only a few results, using numerical Lypunov functions, which guarantee robust stability of systems with unmodeled dynamics in this linearization technique .…”

Quality control of binary distillation columns via nonlinear aggregated models

“…In the "control stream", many papers have been published and the field can be divided into many parts, in particular: linear predictive control [see for example Morari (1988) and Georgiou et al (1988)]; adaptive control [see for example Agarwal and Seborg (1987)]; linear geometric control [see the pioneering work, using Wonham's (1974) approach, of Takamatsu et al (1979) and more recently Kummel and Andersen (1987)]; and the nonlinear geometric approach [see Gauthier et al (1983) and LEvine and Rouchon (1986)] applying the methods of Hirschorn (1981) and Isidori et al (1981); see also the nonlinear control approach of Alsop and Edgar (1987) based on the approaches of Jakubczyk and Respondek (1980), Hunt et al (1983) and Krener (1984). Another related approach on extensive variable control can be found in Georgakis (1986).…”