Investigation of artificial neural networks for modeling, identification and control of nonlinear plant

Abstract

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.