A novel robust adaptive controller for EAF electrode regulator system based on approximate model method

Abstract

The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative (PID) can not meet the requirements. A robust adaptive neural network controller (RANNC) for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 °C/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller (INNC) and PID controller (PIDC).

Keywords

approximate model / electric arc furnaces / nonlinear control / normalized radial basis function neural network (NRBFNN)

References

[1] BillingsS. A., NicholsonH.. Temperature weighting adaptive controller for electric arc furnaces [J]. Ironmaking and Steelmaking, 1977, 4: 216-221

[2] HauksdóttirA. S., GestssonA., VésteinssonA.. Current control of a three-phase submerged arc ferrosilicon furnace [J]. Control Engineering Practice, 2002, 10(4): 457-463

[3] StaibW. E., BlissN. G.. Neural network control system for the electric arc furnaces [J]. Metallurgical Plant and Technology International, 1995, 18(2): 58-60

[4] ZhangS.-de.. Decoupling control for electrode system in electric arc furnace based on neural network inverse identification [C]. Sixth International Conference on Intelligent Systems Design and Applications, 2006Ji’nanIEEE Press112-116

[5] GuanP., LiJ.-c., LiuX.-he.. Direct adaptive fuzzy sliding mode control of arc furnace electrode regulator system [C]. Chinese Control and Decision Conference, 2009GuilinIEEE Press2776-2781

[6] HeQ.-h., HaoP., ZhangD.-qing.. Modeling and parameter estimation for hydraulic system of excavator’s arm [J]. Journal of Central South University of Technology, 2008, 15(3): 382-386

[7] WuL.-b., WangX.-y., LiQiang.. Fuzzy-immune PID control of a 6-DOF parallel platform for docking simulation [J]. Journal of Zhejiang University, 2008, 42(3): 387-391

[8] ZhangY.-w., GuiW.-hua.. Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control [J]. Journal of Central South University of Technology, 2008, 15(2): 256-263

[9] WangY., MaoZ.-z., TianH.-x., LiY., YuanPing.. Modeling of electrode system for three-phase electric arc furnace [J]. Journal of Central South University of Technology, 2010, 17(3): 560-565

[10] PaivaR. P.Modeling and control of an electric arc furnace [D], 1999LundDepartment of Automatic Control Lund Institute of Technology17-18

[11] YuanX.-f., WangY.-n., WuL.-hong.. SVM-based approximate model control for electronic throttle valve [J]. IEEE Transactions on Vehicular Technology, 2008, 57(5): 2747-2756

[12] GeS. S., ZhangJ., LeeT. H.. Adaptive MNN control for a class of non-affine NARMAX systems with disturbances [J]. Systems & Control Letters, 2004, 53(1): 1-12

[13] LiH.-x., DengHua.. An approximate internal model-based neural control for unknown nonlinear discrete processes [J]. IEEE Transactions on Neural Networks, 2006, 17(3): 659-670

[14] YuanX.-f., WangY.-nan.. A novel electronic throttle valve controller based on approximate model method [J]. IEEE Transactions on Industrial Electronics, 2009, 56(3): 883-890

[15] CowperM. R., MulgrewB., UnsworTh.. Nonlinear prediction of chaotic signals using a normalized radial basis function network [J]. Signal Processing, 2002, 82(5): 775-789

[16] AdetonaO., SathananthanS., KeelL. H.. Robust adaptive control of nonaffine nonlinear plants with small input signal changes [J]. IEEE Transactions on Neural Networks, 2004, 15(2): 408-416

[17] SpoonerJ. T., MaggioreM., Ordonez, PassinoK. M.Stable adaptive control and estimation for nonlinear systems [M], 2002New YorkWiley445-446

[18] DengH., LiH.-xiong.. Feedback-linearization-based neural adaptive control for unknown nonaffine nonlinear discrete-time systems [J]. IEEE Transactions on Neural Networks, 2008, 19(9): 1615-1625

[19] LiL., MaoZ.-z., JiaM.-x., LiuFang.. Support vector machine based inverse internal model controller for electric arc furnace [J]. Control Theory & Application, 2010, 27(11): 1455-1462

[20] FuY., ChaiT.-you.. Nonlinear multivariable adaptive control using multiple models and neural networks [J]. Automatica, 2007, 43(6): 1101-1110