Parallel Cascade Control Using Intelligent Techniques

Abstract

Automatic control is important for many engineering problems like Process control, aircraft control, electric drives etc. because of the presence of uncertainties and unforeseen changes in system parameters and input signals. These structural perturbations and environmental variations result in the degradation of system performance. In process industries, cascade control is widely used to reduce the effects of possible disturbances and to improve the dynamic performance of the cascade loop system. Cascade control of parallel type arises when the manipulated and disturbance variables simultaneously affect primary and secondary outputs. Most industrial processes and the primary loop in cascade control systems in particular, are nonlinear, multivariable, distributed complex dynamic systems. There are also large delays in dynamic channels and cross relationship between control parameters. Process dead time and gain are nonlinear and uncertain because they vary according to process conditions. Due to interconnections, dead time and nonlinearity, the control of these systems become difficult. Also, accurate process model that describe the dynamics of the plant is not known. In all such situations a conventional controller cannot maintain system performance at acceptable levels, whereas Fuzzy Logic Controller (FLC) has the capability to model, imprecise information through linguistic expressions. Hence this study suggested a need for the development of fuzzy logic based parallel cascade control system. In this thesis initially a fuzzy logic based control is designed for the primary controller of a parallel cascade control system and its performance has been evaluated by conducting experiments on three different standard system models available in literature. The use of fuzzy logic controller is shown to be more effective than the conventional controller. It contributes to achieve quick response and high accuracy to input parameter fluctuations. ...