Saturday , June 23 2018

Intelligent Gauge Control System Using ARM and Fuzzy PI Controller

Tao GONG, Lei QI
College of Information S. & T., Donghua University
Shanghai, 201620, China taogong@dhu.edu.cn

Abstract: The longitudinal strip thickness error is an important problem in the hydraulic cold rolling industries, and the random disturbances may increase this error. To decrease this error, we proposed a new ARM9-based gauge control system, which is better than traditional automatic gauge control (AGC) system. This ARM9-based control system was comprised of an ARM9, three sensors, a fuzzy PI controller and other devices. The traditional AGC system was uncertain to deal with this error because the parameters were adjusted by human operators. This new ARM9-based control system had better chip such as ARM9 and used fuzzy controller to transform the uncertain information about this error into some certain data. Moreover, this new ARM9-based control system can be smaller and easier to carry. The experimental results were made with the Matlab tool, and the data show that this new ARM9-based gauge control system performs better than the traditional AGC system and is more robust than the traditional one against the distances.

Keywords: ARM9; fuzzy PI controller; gauge control; error; AGC.

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CITE THIS PAPER AS:
Tao GONG, Lei QI,  Intelligent Gauge Control System Using ARM and Fuzzy PI Controller, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (1), pp. 43-50, 2013.

Introduction

In the hydraulic cold rolling industries, the longitudinal thickness control system of strips is generally named AGC system (Automatic Gauge Control system) [1-10]. The control core of this AGC system is the automatic position control (APC) system of the cylinder. Based on the APC, the device for measuring the gauge is set at the output position, in order to measure the gauge for the output part of the strip. But due to the inertia and the delay, the traditional gauge control caused the longitudinal strip thickness error, which decreased the effectiveness of this control process [11].

As we know, the hydraulic cold rolling mill consists of screw-down system, backup roll, work roll and all kinds of sensors. To generate power, the screw-down system is made up of cylinder, servo valve and the sensors of pressure and position. The pressure sensors are used in the feedback control of pressure. In a similar way, with position sensors, the screw-down system can track the position of the cylinder accurately. Neither position nor pressure control of the screw-down system can be directly used to control the thickness errors of the sheet strips.

In order to control the thickness errors of the sheet strips, the current gauge of sheet strips should be measured and the error signals should be used to correct the actual output of the computer controller. So in the AGC hydraulic computer control system, there are two thickness gauges. One is used for the strip thickness before stand, and the other is for the strip thickness after stand.

Up to now, the traditional PI control has taken a useful role for the gauge control in the hydraulic cold rolling industries, but an increasing number of problems on these errors are challenging this traditional control method. Due to the wrong experience of the operators and some random disturbance to the machine, the ineffective control may cause some damages to the control object and the operators. Recently the electromagnetic disturbance has been tested in decreasing the effectiveness of the AGC system, so a new control system is necessary to adjust the parameters by itself against the disturbance. For example, a control algorithm is proposed with an artificial neural network [12]. In this paper, an intelligent gauge control system is designed on the Advanced RISC Machines (ARM) and some intelligent PI control strategies [13].

Roadmap validation approach and results are discussed in Section 5. The contribution of the collaborative networks discipline for the provision of integrated care services is discussed in Section 6. Implementation aspects and conclusions complement the paper.

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https://doi.org/10.24846/v22i1y201305