Development of multilayer neural network for real time temperature control system

Since last two decade a substantial amount of research has been reported in the field of identification and control of nonlinear dynamical systems using artificial neural networks. The work presented in this paper comprises the study of the development of multilayer neural network for real time temperature control system. Inverse dynamic modelling approach is used as a basic control strategy. The primary aim is to construct a network that allows for modeling of a global dynamics of a system. The study devoted to efficient identification and control design methods using neural network based nonlinear control for water bath temperature, and to implement them in a real world environment. The performance of the control systems was verified by simulations and with real-time experiments using pilot process.

I. Introduction

Temperature is a basic physical quantity, which is measured and controlled. There is a need to control temperature in various fields such as medical, biological, industrial and many times in basic scientific research. During the last four to five decades, considerable effort has been devoted to the study of temperature controllers from on-off to intelligent one. The past four decades have witnessed major advances in systems theory made through a combination of mathematics, modelling, computation, and experimentation. During the past ten to twelve years, there has also been an explosive growth in pure and applied research related to Neural Networks (NN) [1]–[2].