Improved Model Reference Adaptive and Its Application in Grinding Process

doi:10.16182/j.issn1004731x.joss.201804049

Abstract

Abstract: Aiming at the characteristics between a ball mill and a grader during the grinding process such as multi variable, strong-coupling and parameter time variation, after decoupling the mathematical model, a model reference adaptive control (MRAC) method for online automatic gain adjustment by differential evolution algorithm is proposed. Due to return sand and water supply and other factors bringing uncertainty to the controlled object model, it needs to adjust constantly adaptation gain online through the differential evolution so as to overcome the effects of parameter variations in mine operation process and various kinds of interference to system. Simulation results show that the control method performs a smaller overshoot, quicker responsivity and better robustness than the model reference adaptive control with gradient method; the stable control of the grader overflow concentration is achieved; and the model reference adaptive control method with differential evolution algorithm is effective and practical.

Key words: differential evolution, reference model, adaptive control, decoupling

CLC Number:

TP181

Zhou Ying, Chen Yang, Yue Bin. Improved Model Reference Adaptive and Its Application in Grinding Process[J]. Journal of System Simulation, 2018, 30(4): 1608-1614.

References

[1] 卢绍文, 余策. 磨矿粒度动态过程的一种快速Monte Carlo仿真方法[J]. 自动化学报, 2014, 40(9): 1903-1911.
Lu Shaowen, Yu Ce.A Fast Monte Carlo Algorithm for Dynamic Simulation of Particle Size Distribution of Grinding Processes[J]. Acta Automatica Sinica, 2014, 40(9): 1903-1911.
[2] Jian Tang, Tianyou Chai, Wen Yu, et al.Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective[J]. IEEE transactions on automation science and engineering(S1545-5955), 2013, 10(3): 726-740.
[3] 周平. 磨矿过程运行反馈控制[M]. 北京: 科学出版社, 2015: 50-51.
Zhou Ping.Operational feedback control for Grinding Process[M]. Beijing: Science Press, 2015: 50-51.
[4] 刘潇. 多目标差分进化混合算法研究及其在磨矿分级中的应用[D]. 长沙: 中南大学, 2011.
Liu Xiao.Research of a Hybrid DE Algorithm for Multi-objective Optimization Problems and its Application in Grinding and Classification Process[D]. Changsha: Central South University, 2011.
[5] 赵大勇, 柴天佑. 自适应内模控制方法在磨矿过程中的应用[J]. 控制工程, 2009, 16(4): 426-428.
Zhao Dayong, Chai Tianyou.Adaptive Internal Model Control Method and Its Application to Grinding Process[J]. Control Engineering, 2009, 16(4): 426-428.
[6] 任金霞, 王挺. 基于改进型粒子群优化的磨矿分级系统溢流浓度控制的研究[J]. 矿山机械, 2012, 9(5): 92-95.
Ren Jinxia, Wang Ting.Study on over? ow density control of milling and classiﬁ cation system based on improved particle swarm optimization[J]. Micollurgical Machinery, 2012, 9(5): 92-95.
[7] Shaowen Lu, Ping Zhou, Tianyou Chai, et al.Modeling and Simulation of Whole Ball Mill Grinding Plant for Integrated Control[J]. IEEE transactions on automation science and engineering(S1545-5955), 2014, 11(4): 1004-1019.
[8] 赵大勇, 岳恒, 周平, 等. 基于智能优化控制的磨矿过程综合自动化系统[J]. 山东大学学报, 2005, 35(3): 119-124.
ZHAO Dayong, YUE Heng, ZHOU Ping, et al.Integrated Automation System of Grinding Process Based on Intelligent Optimizing Control[J] .Journal of Shandong University, 2005, 35(3): 119-124.
[9] 李勇. 磨矿过程参数软测量与综合优化控制的研究[D]. 大连: 大连理工大学, 2006.
Li Yong.Research on the Parameters’ Soft Sensing and Synthetic Optimizing Control of Grinding Process [D]. Dalian: Dalian University of Technology, 2006.
[10] 杨燕, 曹建光, 田志宏, 等. 交流电机模型参考自适应控制系统的仿真[J]. 电气传动, 2007, 37(5): 13-15.
Yang Yan, Cao Jianguang, Tian Zhihong, et al.Simulation of Model Reference Adaptive Control System in AC Motor Control[J]. Electric Drives, 2007, 37(5): 13-15.
[11] Yongming Li, Shaocheng Tong.Adaptive Fuzzy Output-Feedback Control of Pure-Feedback Uncertain Nonlinear Systems With Unkonwn Dead Zone[J]. IEEE transaction on fuzzy systems(S1064-1246), 2014, 22(5): 1341-1347.
[12] 戈剑武, 祁荣宾, 钱锋, 等. 一种改进的自适应差分进化算法[J]. 华东理工大学学报, 2009, 35(4): 600-605.
Ge Jianwu, Qi Rongbin, Qian Feng, et al.A Modified Adaptive Differential Evolution Algorithm[J]. Journal of East China University of Science and Technology, 2009, 35(4): 600-605.
[13] 杨启文, 蔡亮, 薛云灿, 等. 差分进化算法综述[J]. 模式识别与人工智能, 2008, 21(4): 506-513.
Yang Qiwen, Cai Liang, Xue Yuncan, et al.A Survey of Differential Evolution Algorithms[J]. Journal of Pattern Recognition and Artificial Intelligence, 2008, 21(4): 506-513.
[14] Nyambayar Baatar, Dianhai Zhang, Chang-Seop Koh, et al.An Improve Differential Evolution Algorithm Adopting λ-Best Mutation Strategy for Global Optimization of Electromagnetic Devices[J]. IEEE transactions on Magazines(S0018-9464), 2013, 49(5): 2097-2010.
[15] 王朋, 刘林, 陈哲, 等. 基于改进DE算法的PID参数整定及其应用[J]. 动力工程学报, 2015, 35(3): 191-196.
Wang Peng, Liu Lin, Chen Zhe, et al.PID Parameter Tuning Based on Improved DE Algorithm and the Application[J]. Journal of Chinese Society of Power Engineering, 2015, 35(3): 191-196.