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.

>>Full Text
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. https://doi.org/10.24846/v22i1y201305

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.

REFERENCES

MASON, J. O. III, S. R. GUPTA, C. J. COMPTON, Comparison of Hemorrhagic Complications of Warfarin and Clopidogrel Bisulfate in 25-Gauge Vitrectomy versus a Control Group, Ophthalmology, vol. 118, no. 3, 2011, pp. 543-547.
SCHOENE, T., J. ILLIGNER, P. MANURUNG, GPS-controlled Tide Gauges in Indonesia – a German Contribution to Indonesia’s Tsunami Early Warning System, Natural Hazards and Earth System Sciences, vol. 11, no. 3, 2011, pp. 731-740.
MURAKAMI, A., M. NAKAYAMA, Y. MAEDA, Tension Reference Optimization in Automatic Gauge and Tension Control for a Tandem Cold Mill, Tetsu To Hagane – Journal of the Iron and Steel Institute of Japan, vol. 96, no. 10, 2010, pp. 601-607.

NIKOLSKII, A. A., V. V. KOROLEV, D. Y. MURINETS, Features in the Control of The Cross-sectional Profile of Pistons on Out-of-Round Gauges with Model Rotation of the Spindle, Measurement Techniques, vol. 53, no. 2, 2010, pp. 156-165.
WANG, Q. S., Active Buckling Control of Beams using Piezoelectric Actuators and Strain Gauge Sensors, Smart Materials & Structures, vol. 19, no. 6, 2010.
DONG, M., C. LIU, G. Y. LI, Robust Fault Diagnosis Based on Nonlinear Model of Hydraulic Gauge Control System on Rolling Mill, IEEE Transactions on Control Systems Technology, vol. 18, no. 2, 2010, pp. 510-515.
ROMAN, N., E. CEANGA, I. BIVOL, Adaptive Automatic Gauge Control of a Cold Strip Rolling Process, Advances in Electrical and Computer Engineering, vol. 10, no. 1, 2010, pp. 7-17.
ZHANG, D. H., H. ZHANG, T. SUN, Monitor Automatic Gauge Control Strategy with a Smith Predictor for Steel Strip Rolling, Journal of University of Science and Technology Beijing, vol. 15, no. 6, 2008, pp. 827-832.
LINGHU, K. Z., A. R. HE, Q. YANG, Dynamic Decoupling for Combined Shape and Gauge Control System in Wide Strip Rolling Process, Journal of Iron and Steel Research International, vol. 15, no. 2, 2008, pp. 28-31.
YANG, B. H., W. D. YANG, L. G. CHEN, Dynamic Optimization of Feedforward Automatic Gauge Control based on Extended Kalman Filter, Journal of Iron and Steel Research International, vol. 15, no. 2, 2008, pp. 39-42.
GONG, T., L. QI, Novel ARM-Based Gauge Control System with Fuzzy PI Controller, International Journal of Multimedia and Ubiquitous Engineering, vol. 7, no. 2, 2012, pp. 527-532.
ZHANG, Y. C., H. Q. LIANG, X. J. HU, EMC Study of an ARM-based Electronic Control Unit for High Power DC/DC Converter, IEEE Vehicle Power and Propulsion Conference, 2008.
QI, L., T. GONG, Gauge Control System Based on ARM and Fuzzy PI Controller, Chinese patent, 2011.
CHAIYATHAM, T., I. NGAMROO, A Bee Colony Optimization Based-Fuzzy Logic-Pid Control Design of Electrolyzer For Microgrid Stabilization, International Journal of Innovative Computing Information and Control, vol. 8, no. 9, 2012, pp. 6049-6066.
DINH, Q. T., Q. T. TRUONG, K. K. AHN, , Development of a Novel Linear Magnetic Actuator with Trajectory Control based on an Online Tuning Fuzzy PID Controller, International Journal of Precision Engineering and Manufacturing, vol. 13, no. 8, 2012, pp. 1403-1411.
PARIS, B., J. EYNARD, S. GRIEU, Hybrid PID-fuzzy Control Scheme for Managing Energy Resources in Buildings, Applied Soft Computing, vol. 11, no. 8, 2011, pp. 5068-5080.
DINH, Q. T., K. K. AHN, Parallel Control for Electro-Hydraulic Load Simulator Using Online Self Tuning Fuzzy PID Technique, Asian Journal of Control, vol. 13, no. 4, 2011, pp. 522-541.
TRUONG, D. Q., K. K. AHN, Force Control for Press Machines using an Online Smart Tuning Fuzzy PID based on a Robust Extended Kalman Filter, Expert Systems with Applications, vol. 38, no. 5, 2011, pp. 5879-5894.
REN, X. Y., F. S. DU, H. G. HUANG, Application of Improved Fuzzy Immune PID Controller to Bending Control System, Journal of Iron and Steel Research International, vol. 18, no. 3, 2011, pp. 28-33.
JAHEDI, G., M. M. ARDEHALI, Genetic Algorithm-based Fuzzy-PID Control Methodologies for Enhancement of Energy Efficiency of a Dynamic Energy System, Energy Conversion and Management, vol. 52, no. 1, 2011, pp. 725-732.
MOHAN, B. M., Fuzzy PID Control via Modified Takagi-Sugeno Rules, Intelligent Automation and Soft Computing, vol. 17, no. 2, 2011, pp. 165-174.
PAN, I., S. DAS, A. GUPTA, Tuning of an Optimal Fuzzy PID Controller with Stochastic Algorithms for Networked Control Systems with Random Time Delay, ISA Transactions, vol. 50, no. 1, 2011, pp. 28-36.
CETIN, S., A. V. AKKAYA, Simulation and Hybrid Fuzzy-PID Control for Positioning of a Hydraulic System, Nonlinear Dynamics, vol. 61, no. 3, 2010, pp. 465-476.
WANG, W., Z. M. QIU, Analysis of Characteristics of Depressing System of Rolling Mill Hydraulic Pressure, Journal of Changsha University, vol. 24, no. 2, 2010, pp. 42-43.
SUN, M. H., Y. Q. WANG, W. ZHANG, Research on Cascade Predictive Control in Hydraulic AGC of Cold Rolling Mill, Proceedings of 2007 2nd IEEE Conference on Industrial Electronics and Applications, 2007.
ALFARO, V. M., R. VILANOVA, Performance/Robustness Trade-off Design Frame-work for 2DoF PI Controllers, Studies in Informatics and Control, vol. 21, no. 1, 2012, pp. 75-83.