磨矿工艺过程优化控制仿真研究
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摘要
针对磨矿过程中多变量、强干扰、非线性等特点,提出一种改进模糊T-S模型的内模控制的方法。将此算法应用到再磨过程旋给浓度控制回路中,使旋给浓度稳定在一定的范围内,保证磨矿粒度的合格率。通过将变论域的方法与规则修剪的方法相结合改进T-S模糊模型,并将改进后的模糊T-S模型作为内部模型,其逆模型作为内模控制器,并验证了磨矿控制系统的稳定性和鲁棒性。仿真结果表明,改进方法的调节时间短、超调量小、鲁棒性好,验证了所提控制方法的有效性。
Aiming at the characteristics of multi-variable,strong disturbance and nonlinear in grinding process,an internal model control method based on improved T-S fuzzy model is proposed. The proposed algorithm is applied to the pumping concentration control loop in the regrinding process to stabilize the pumping concentration within a certain range,and the qualified rate of the grinding particle size is guaranteed. The fuzzy T-S model is improved by combining the variable universe with the rule pruning method,and the improved model is taken as the internal model,and the inverse improved model is used as the internal model controller. Then,the stability and robustness of the system are verified. The simulation results show that the proposed method has the advantages of short regulation time,small overshoot and good robustness. The validity of the proposed control method is verified.
Aiming at the characteristics of multi-variable,strong disturbance and nonlinear in grinding process,an internal model control method based on improved T-S fuzzy model is proposed. The proposed algorithm is applied to the pumping concentration control loop in the regrinding process to stabilize the pumping concentration within a certain range,and the qualified rate of the grinding particle size is guaranteed. The fuzzy T-S model is improved by combining the variable universe with the rule pruning method,and the improved model is taken as the internal model,and the inverse improved model is used as the internal model controller. Then,the stability and robustness of the system are verified. The simulation results show that the proposed method has the advantages of short regulation time,small overshoot and good robustness. The validity of the proposed control method is verified.
引文
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[12] W Cheng,C Juang. An incremental support vector machinetrained TS-type fuzzy system for online classification problems[J]. Fuzzy Sets and Systems,2011,163(1):24-44.
[13] B Zhu,et al. A GMDH-based fuzzy modeling approach for constructing T-S model[J]. Fuzzy Sets and Systems,2012,189(1):19-29.
[14]廖龙涛,李少远,黄广斌.规则可剪的非线性系统T-S模糊模型辨识[J].自动化学报,2007,33(10):1097-1100.
[15]张伟,毛剑琴.基于模糊树模型的非线性系统内模控制[J].控制理论与应用,2013,30(04):463-468.
[16]周平,柴天佑,陈通文.工业过程运行的解耦内模控制方法[J].自动化学报,2009,35(10):1362-1368.
[17]孔令军,张晓,李晓迅,李成君.并联型有源电力滤波器直流侧电压模糊内模控制方法[J].电工技术学报,2011,(S1):224-228.
[18]刘琪,黄贞贞.模糊内模控制在磨矿系统中的应用[J].科学技术与工程,2012,29(12):7674-7676.
[2] S Diehl. A regulator for continuous sedimentation in ideal clarifier–thickener units[J]. Journal of Engineering Mathematics,2008,60(3):265-291.
[3] W Dai,et al. Data-Driven Optimization Control for Safety Operation of Hematite Grinding Process[J]. IEEE Transactions on Industrial Electronics,2015,62(5):2930-2941.
[4]张红燕,殷利平,张永宏.磨矿粒度分布的概率密度函数跟踪控制研究[J].计算机仿真,2014,31(10):364-368.
[5]李海波,等.混合选别浓密机底流矿浆浓度和流量区间智能切换控制方法[J].自动化学报,2014,40(9):1967-1975.
[6] M Duarte,et al. A comparative experimental study of five multivariable control strategies applied to a grinding plant[J]. Powder Technology,1999,104(1):1-28.
[7] J P Segovia,F Concha,D Sbarbaro. On the control of sludge level and underflow concentration in industrial thickeners[J]. IFAC Proceedings Volumes,2011,44(1):8571-8576.
[8]李明旭,周颖,赵慧敏,陈阳.补偿模糊神经网络在磨矿控制中的应用研究[J].计算机仿真,2015,32(9):340-344.
[9]赵大勇,柴天佑.再磨过程泵池液位区间与给矿压力模糊切换控制[J].自动化学报,2013,39(5):556-564.
[10]李西平.磨矿工业过程智能控制算法与仿真研究[D].西安建筑科技大学,2006.
[11] H Ying. General SISO Takagi-Sugeno fuzzy systems with linear rule consequent are universal approximators[J]. IEEE Transactions on Fuzzy Systems,1998,6(4):582-587.
[12] W Cheng,C Juang. An incremental support vector machinetrained TS-type fuzzy system for online classification problems[J]. Fuzzy Sets and Systems,2011,163(1):24-44.
[13] B Zhu,et al. A GMDH-based fuzzy modeling approach for constructing T-S model[J]. Fuzzy Sets and Systems,2012,189(1):19-29.
[14]廖龙涛,李少远,黄广斌.规则可剪的非线性系统T-S模糊模型辨识[J].自动化学报,2007,33(10):1097-1100.
[15]张伟,毛剑琴.基于模糊树模型的非线性系统内模控制[J].控制理论与应用,2013,30(04):463-468.
[16]周平,柴天佑,陈通文.工业过程运行的解耦内模控制方法[J].自动化学报,2009,35(10):1362-1368.
[17]孔令军,张晓,李晓迅,李成君.并联型有源电力滤波器直流侧电压模糊内模控制方法[J].电工技术学报,2011,(S1):224-228.
[18]刘琪,黄贞贞.模糊内模控制在磨矿系统中的应用[J].科学技术与工程,2012,29(12):7674-7676.