Multivariable Identification Methods Applied
Multivariable Identification. Linear Identification. NonLinear Identification.
The identification and control ofmultivariable processes currently are areas of growing
interest in control systems community, yet. Classical control methods commonly used in
the industry, have limitations when applied to multivariable processes with complex features,
such as: Nonlinearities, unstability, time delay, non-stationary behavior and coupling
between variables. Thus, it is evident the need for mathematical models that represent
these features with the highest possible fidelity, to be used other control approaches nonconventional,
such as such as predictive and intelligent controllers. This doctoral thesis
proposal will show the basic fundamentals that are being followed to find contributions
in the area of multivariable linear identification and/or multivariable nonlinear identification.
Initially, It is intended to find more simplified and optimized hybrid forms that
help primarily in developing practical systems for industry. However, it is not disposed
to contribute in other areas of identification, for example in the area of excitation signals
using the relay Aparatus changed. Will also be presented partial results, although shy,
implementation of multivariable linear identification, whose algorithms are serving as an
axiom to advance about multivariable nonlinear identification.