Citation:
Cao, Yi, Direct and indirect gradient control for static optimisation.
International Journal of Automation and Computing, 2006, Vol. 2 No. 1 pp 60-66
Abstract:
Static “self-optimising” control is an important concept, which provides a link
between static optimisation and control (Skogestad, 2000). According to the
concept, a dynamic control system could be configured in such a way that when a
set of certain variables are maintained at their setpoints, the overall process
operation is automatically optimal or near optimal at steady-state in the
presence of disturbances. A novel approach using constrained gradient control to
achieve “self-optimisation” has been proposed by Cao (2004). However, for most
process plants, the information required to get the gradient measure may not be
available in real-time. In such cases, controlled variable selection has to be
carried out based on measurable candidates. In this work, the idea of direct
gradient control has been extended to controlled variable selection based on
gradient sensitivity analysis (indirect gradient control). New criteria, which
indicate the sensitivity of the gradient function to disturbances and
implementation errors, have been derived for selection. The particular case
study shows that the controlled variables selected by gradient sensitivity
measures are able to achieve near optimal perf