Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2009 / In real world systems such as the waste water treatment plants, the nonlinearities,
uncertainty and complexity playa major role in their daily operations. Effective control of such
systems variables requires robust control methods to accommodate the uncertainties and
harsh environments. It has been shown that intelligent control systems have the ability to
accommodate the system uncertain parameters. Techniques such as fuzzy logic, neural
networks and genetic algorithms have had many successes in the field of control because
they contain essential characteristics needed for the design of identifiers and controllers for
complex systems where nonlinearities, complexity and uncertainties exist.
Approaches based on neural networks have proven to be powerful tools for solvinq nonlinear
control and optimisation problems. This is because neural networks have the ability to learn
and approximate nonlinear functions arbitrarily wei!. The approximation capabilities of such
networks can be used for the design of both identifiers and controllers. Basically, an artificial
neural network is a computing architecture that consists of massively parallel
interconnections of simple computing elements that provide insights into the kind of highly
parallel computation that is carried out by biological nervous system. A large number of
networks have been proposed and investigated with various topological structures.
functionality and training algorithms for the purposes of identification and control of practical
systems.
For the purpose of this research thesis an approach for the investigation of the use of neural
networks in identification, modelling and control of non-linear systems has been carried out.
In particular, neural network identifiers and controllers have been designed for the control of
the dissolved oxygen (DO) concentration of the activated sludge process in waste water
treatment plants. These plants, being complex processes With several variables (states) and
also affected by disturbances require some form of control in order to maintain the standards
of effluent. DO concentration control in the aeration tank is the most widely used controlled
variable. Nonlinearity is a feature that describes the dynamics of the dissolved oxygen
process and therefore the DO estimation and control may not be sufficiently achieved with a
conventional linear controller.
Neural networks structures are proposed, trained and utilized for purposes of identification.
modelling and design of NN controllers for nonlinear DO control. Algorithms and programs
are developed using Matlab environment and are deployed on a hardware PLC platform. The
research is limited to the feedforward multilayer perceptron and the recurrent neural
networks for the identification and control. Control models considered are the direct inverse mode! control, internal mode! contra! and feedback linearizing control. Real-time
implementation is limited to the lab-scale wastewater treatment plant.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:cput/oai:localhost:20.500.11838/1097 |
| Date | January 2009 |
| Creators | Muga, Julius N'gon'ga |
| Publisher | Cape Peninsula University of Technology |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | http://creativecommons.org/licenses/by-nc-sa/3.0/za/ |
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