Directionality and Nonlinearity - Challenges in Process Control
Thesis for the degree Doctor of Technology
by
Jonas B. Waller
2003
Abstract
The use of the concepts of directionality and ill-conditionedness is investigated.
A lack of consistent terminology is detected, and some clarifications to
the terminology are suggested. Robustness issues with respect to directional-ity
are discussed. Control structures in the form of decoupling are discussed,
and a more general formulation for dynamic decoupling is formulated. Process
nonlinearity, especially in the form of operating region dependent dynamics,
is tackled by the use of a nonlinear input/output model of quasi-ARMAX
type. The model is applied in the model predictive control framework for control
of nonlinear processes, with a pH-process as a case-study. The nonlinear
controller is also reformulated for the multivariable case, and applied to control
of an ill-conditioned, nonlinear distillation column. The computational
demand of the model predictive control formulation is addressed by studying
methods to approximate the behaviour of model-predictive controllers by
the use of neural networks as function approximators. Also, as a means to
decrease the online computational demands, a neurodynamic programming
formulation is investigated in the sense that the future cost-to-go function is
calculated by use of offline MPC calculations and then approximated.