Ahmed HAFAIFA, Attia DAOUDI, Kouider LAROUSSI
Industrial Automation and Diagnosis Systems Laboratory,
Department of Science and Technology,
Science and Technology Faculty,
University of Djelfa 17000 DZ Algeria
hafaifa@hotmail.com; a_daoudi@hotmail.com; kouider-laro@hotmail.com

Abstract: This paper presents the problem of robust design of surge controller in centrifugal compression system using fuzzy techniques. The use of fuzzy techniques in modelling and control has been important to reduce costs and losses of the system. In this paper, based on the fuzzy modelling and control, a new way to establish fuzzy logic controller is proposed. In this paper, the performance of the fuzzy controller has been designed to give a new control technique of surge in centrifugal compression system. The validation results show the effectiveness of the proposed approach in order to achieve desired performance.

Keywords: Compression system, centrifugal compressor, surge control, supervision system, fuzzy modelling, fuzzy control, surge phenomena.

>Full text
CITE THIS PAPER AS:
Ahmed HAFAIFA, Attia DAOUDI, Kouider LAROUSSI, Modelling and Control of Surge in Centrifugal Compression Based on Fuzzy Rule System, Studies in Informatics and Control, ISSN 1220-1766, vol. 19 (4), pp. 347-356, 2010.

1. Introduction

In recent years fuzzy logic has been an active area of research, many research efforts have been focused on fuzzy modelling and control issues based on the Takagi-Sugeno (TS) fuzzy model [2][3][8] and [12], which is described by fuzzy IF-THEN rules. For a nonlinear system that is transformed successfully into a T-S fuzzy model, the stability of the overall nonlinear system still cannot be guaranteed even if each subsystem of the T-S fuzzy model is stable. In this work, we propose a new fuzzy controller design for the supervision of a dynamical complex system, based on dynamic fuzzy model for control and supervision. The idea of model based fuzzy observers in supervision is to compare output signals of the model with the real measurements available in the process, thereby generating the indicators, which are fault indicators giving information about the location and timing of a fault.

This fuzzy supervisory approach requires precise mathematical relationships relating the model to the process, to allow the detection of small abrupt and incipient faults quickly and reliably. Many standard observer-based techniques exist in the literature providing different solutions to both the theoretical and practical aspects of supervision problem for linear and nonlinear system [1], [6] and [13]. Many of these procedures are based on the design of an unknown input observer robust with respect to the disturbances. If the disturbances and modelling errors are not properly taken into account in the estimation process, it is then likely that any attempt in monitoring the system’s health based on the observer leads to numerous false alarms. From this issue, many results had been obtained in order to get a representation of non linear systems under the form of multiple linear sub-models aggregated by fuzzy logic [9].

This paper describes tools and techniques that can and have been used to model transient flows and performance, mechanical and the minimum response time in compressor system control. The tools used by fuzzy logic include a method of characteristic transient flow analysis routine and finite time step programs that simulate control systems, valve actuators, and the opening (or closing) rate of valves with the resulting flows. The effects of volumes and lengths of station piping, scrubbers, and coolers including temperature effects are accounted for. Fuzzy logic models and control also track the performance of centrifugal compressors at different speeds, account for the rotation inertia of compressor trains, and evaluate the thermo physical properties of gas streams.

REFERENCES:

1. Abdel-Hady, F., S. Abuelenin, Design and Simulation of a Fuzzy-Supervised PID Controller for a Magnetic Levitation System, Studies in Informatics and Control Journal, ICI Publishing House, vol. 17, no. 3, 2008, pp. 315-328.