Neural adaptive PSD decoupling controller and its application in three-phase electrode adjusting system of submerged arc furnace

Abstract

Taking three-phase electrode adjusting system of submerged arc furnace as study object which has nonlinear, time-variant, multivariable and strong coupling features, a neural adaptive PSD(proportion, sum and differential) dispersive decoupling controller was developed by combining neural adaptive PSD algorithm with dispersive decoupling network. In this work, the production technology process and control difficulties of submerged arc furnace were simply introduced, the necessity of establishing a neural adaptive PSD dispersive decoupling controller was discussed, the design method and the implementation steps of the controller are expounded in detail, and the block diagram of the controlled system is presented. By comparison with experimental results of the conventional PID controller and the adaptive PSD controller, the decoupling ability, adaptive ability, self-learning ability and robustness of the neural adaptive PSD dispersive decoupling controller have been testified effectively. The controller is applicable to the three-phase electrode adjusting system of submerged arc furnace, and it will play an important role for achieving the power balance of three-phrase electrodes, saving energy and reducing consumption in the process of smelting.

Keywords

PSD algorithm / decoupling controller / submerged arc furnace / three phase electrode

References

[1] LiM.-j., ZhangFeng.. Electric balance control of the three phases power in smelting region in the sub-merged furnace of the ferroalloys [J]. Ferro-alloys, 2003, 34(5): 22-23

[2] WangB.-jin.. On PID control system design of electrode regulation of submerged arc furnace [J]. Ferro-alloys, 2009, 40(1): 38-40

[3] MarsikJ., StrejicV.. A new conception of digital adaptive PSD control [J]. Problem of Control and Information Theory, 1993, 12(44): 267-279

[4] ALEXIK, MIKULAS. Simulation experiments with self tuning PSD control algorithm [C]// Proceedings of UK Sim 10th International Conference on Computer Modelling and Simulation, Cambridge, UK, 2008: 34–39.

[5] JiaoB., GuX.-sheng.. Improvement and realization of neuron PSD control in servo-control system [J]. Journal of Shanghai Jiaotong University (Science), 2005, 10(2): 143-146

[6] ShuH.-lin.. PID neural network for decoupling control of strong coupling multivariable time-delay systems [J]. Control Theory and Applications, 1998, 15(6): 920-924

[7] ArdehaliM. M., SabooriM., TteshnelabM.. Numerical simulation and analysis of fuzzy PID and PSD control methodologies as dynamic energy efficiency measures [J]. Energy Conversion and Management, 2004, 45(13): 1981-1992

[8] ZhouH., ZhongM.-hui.. A neural intellectual decoupling control strategy for a power plant ball miller [J]. International Journal of Automation and Computing., 2005, 2(1): 43-47

[9] SunJ.-h., WangW., ZhengK.-wei.. Research of PID neural networks decoupling control of marine nuclear power plant [J]. Journal of Harbin Engineering University, 2007, 28(6): 656-659

[10] LiM., LinY.-j., MaY.-guang.Adaptive neural non-model decouple control for MIMO system[J], 2003, 20(3): 68-71

[11] LiuG.-h., LiuP.-y., ShenY., WangF.-liang.. Neural network generalized inverse decoupling control of two-motor variable frequency speed-regulating system [J]. Proceedings of the CSEE, 2008, 28(36): 98-102

[12] MulholandA. C., Brereton-stilesP. J., HockadayC. J.. The effectiveness of current control of submerged arc furnace electrode Penetration in selected scenarios [J]. Journal of The South African Institute of Mining and Metallurgy, 2009, 109(10): 601-607

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

[14] HauksdottirA. S., SoderstromT., ThorfinnssonY. P., GestssonA.. system identification of a three-phase submerged arc ferrosilicon furnace [J]. IEEE Transactions on Control Systems Technology, 1995, 3(4): 377-387

[15] ZhouH.-b., ZhouY.-h., ZhuL.-juan.. Implementation and comparison of improving BP neural network based on MATLAB [J]. Computing Technology and Automation, 2008, 27(1): 28-31