Identification and control of nonlinear processes with static nonlinearities.

Process control has been playing an increasingly important role in many industrial applications as an effective way to improve product quality, process costeffectiveness and safety. Simple linear dynamic models are used extensively in process control practice, but they are limited to the type of process behavior they can approximate. It is well-documented that simple nonlinear models can often provide much better approximations to process dynamics than linear models. It is evident that there is a potential of significant improvement of control quality through the implementation of the model-based control procedures. However, such control applications are still not widely implemented because mathematical process models in model-based control could be very difficult and expensive to obtain due to the complexity of those systems and poor understanding of the underlying physics. The main objective of this thesis is to develop new approaches to modeling and control of nonlinear processes. In this thesis, the multivariable nonlinear processes are approximated using a model with a static nonlinearity and a linear dynamics. In particular, the Hammerstein model structure, where the nonlinearity is on the input, is used. Cardinal spline functions are used to identify the multivariable input nonlinearity. Highlycoupled nonlinearity can also be identified due to flexibility and versatility of cardinal spline functions. An approach that can be used to identify both the nonlinearity and linear dynamics in a single step has been developed. The condition of persistent excitation has also been derived. Nonlinear control design approaches for the above models are then developed in this thesis based on: (1) a nonlinear compensator; (2) the extended internal model control (IMC); and (3) the model predictive control (MPC) framework. The concept of passivity is used to guarantee the stability of the closed-loop system of each of the approaches. In the nonlinear compensator approach, the passivity of the process is recovered using an appropriate static nonlinearity. The non-passive linear system is passified using a feedforward system, so that the passified overall system can be stabilized by a passive linear controller with the nonlinear compensator. In the extended IMC approach, dynamic inverses are used for both the input nonlinearity and linear dynamics. The concept of passive systems and the passivity-based stability conditions are used to obtain the invertible approximations of the subsystems and guarantee the stability of the nonlinear closed-loop system. In the MPC approach, a numerical inverse is implemented. The condition for which the numerical inversion is guaranteed to converge is derived. Based on these conditions, the input space in which the numerical inverse can be obtained is identified. This constitutes new constraints on the input space, in addition to the physical input constraints. The total input constraints are transformed into linear input constraints using polytopic descriptions and incorporated in the MPC design.

Grantor dc:publisher

UNSW, Sydney

Year dc:date

2007

Author dc:creator

• Chan, Kwong Ho

• Chemical process control.
• Chemical processes.
• Predictive control.

Statement dc:rights

• open access
• CC BY-NC-ND 3.0
• free_to_read

Licence

• https://purl.org/coar/access_right/c_abf2
• https://creativecommons.org/licenses/by-nc-nd/3.0/au/

Language dc:language

EN

Identifier

https://doi.org/10.26190/unsworks/17700

OAI identifier oai:identifier

oai:unsworks.library.unsw.edu.au:1959.4/40620