Genetic design of robust predictive control systems

Segayer, AM

Abstract

Despite the fact that PID controllers are undoubtedly the most popular controllers used in
industrial control processes for decades, they do not perform well when applied to systems
with significant time-delay. Consideration of this problem led to the development of
predictive control strategies in the 1950's and lately in the 1980's. Perhaps the best-known
predictive control techniques that has received the most attention in the control of long
time-delayed plants and used almost exclusively in the process industries are the Smith
Predictor Controller (SPC) and more recently the Internal Model controller (IMC). These
two controllers are amenable to conventional robustness measures such as Gain-margin,
Phase-margin, Delay-margin and Modulus-margin, which make them suitable for
comparison with conventional PID type controllers.
The application of Evolutionary Algorithms to process control systems constitutes a new
methodology within the CACSD. In recent years, the controls research community have
become increasingly interested in the use of genetic algorithms as a means to control
various classes of systems, however, the technique has concentrated on the unity feedback
control system design problem in both the SISO and MIMO cases and there are still a lot of
research topics that are not addressed (or properly addressed), in the application of genetic
algorithms. In this research, the genetic algorithm has been adopted as a major control
design tool for designing robust predictive control systems for process plants.
As much work on predictive control has been done using low order Pade' approximatios
(Morari, 1989), it is interesting to further investigate the robustness of IMC and SPC by not
using Pade' approximations and deploying the concept of delay-margin to assess
robustness. In this context, a new design methodology is proposed for designing robust IMC controllers under pre-prescribed gain-margin and delay-margin constraints where
controller tuning is replaced by simple design curves.
The robustness analysis of a single parameter Smith predictor controller (SP-SPC) in
comparison with a proportional plus integral Smith predictor controller (PI-SPC) revealed
that under robustness consideration, TMC controller and Pi-Smith predictor controller are
almost identical, however, IMC is preferred because the design curves developed in this
thesis can be used to automate the design.
hi this thesis also, Competitive Co-evolutionary Algorithm is proposed as a new technique
to design robust IMC controllers that can cope with large parametric uncertainty. The co-
evolutionary technique proposed is capable of determining the most difficult plants to
control, both in terms of stability and performance and can simultaneously select the
optimal nominal parameter values for the model used in the IMC controller.
A new gain-scheduled controller based on Internal model controller architecture is
proposed. This controller is novel in the sense that no controller design phase is needed as
the case with conventional gain-scheduled controller design. The only task required in the
gain-scheduled IMC controller design is the identification and mapping a set of locally
linearised models to fit the non-linear process. The design curves can be used to assure
robustness in terms of GM and DM for all the locally linearised control systems. In this
context, such gain-scheduled controllers have been designed for a non-linear water tank
system and a non-linear heating exchanger (laboratory process trainer).

Citation

Segayer, A. Genetic design of robust predictive control systems. (Thesis). Salford : University of Salford

• Thesis Type: Thesis
• Deposit Date: Oct 3, 2012
• Award Date: Jan 1, 2002