Disturbance rejection control for Raymond mill grinding system based on disturbance observer

In the Raymond mill grinding processes, high-accuracy control for the current of Raymond mill is vital to enhance the product quality and production efficiency as well as cut down the consumption of spare parts. However, strong external disturbances, such as variations of ore hardness and ore size, always exist. It is not easy to make the current of Raymond mill constant due to these strong disturbances. Several control strategies have been proposed to control the grinding processes. However, most of them (such as PID and MPC) reject disturbances merely through feedback regulation and do not deal with the disturbances directly, which may lead to poor control performance when strong disturbances occur. To improve disturbance rejection performance, a control scheme based on PI and disturbance observer is proposed in this work. The scheme combines a feedforward compensation part based on disturbance observer and a feedback regulation part using PI. The test results illustrate that the proposed method can obtain remarkable superiority in disturbance rejection compared with PI method in the Raymond mill grinding processes.

[1]  A. Bhaumik,et al.  Designing an Intelligent Expert Control System Using Acoustic Signature for Grinding Mill Operation , 2006, 2006 IEEE International Conference on Industrial Technology.

[2]  Qi Li,et al.  Constrained model predictive control in ball mill grinding process , 2008 .

[3]  Chris Aldrich,et al.  Neurocontrol of a ball mill grinding circuit using evolutionary reinforcement learning , 2001 .

[4]  Kai Guo,et al.  Disturbance observer based position tracking of electro-hydraulic actuator , 2015 .

[5]  Tianyou Chai,et al.  Modeling and Simulation of Whole Ball Mill Grinding Plant for Integrated Control , 2014, IEEE Transactions on Automation Science and Engineering.

[6]  George W. Irwin,et al.  Neural modelling, control and optimisation of an industrial grinding process , 2005 .

[7]  Hui Li,et al.  Simple disturbance observer for disturbance compensation , 2010 .

[8]  Qibing Jin,et al.  Design of active disturbance rejection internal model control strategy for SISO system with time delay process , 2015 .

[9]  Daniel Hodouin,et al.  A survey of grinding circuit control methods: from decentralized PID controllers to multivariable predictive controllers , 2000 .

[10]  Hong Wang,et al.  Hybrid intelligent control for regrinding process in hematite beneficiation , 2014 .

[11]  Huber Nieto-Chaupis Testing a predictive control with stochastic model in a balls mill grinding circuit , 2014, 2014 11th IEEE/IAS International Conference on Industry Applications.

[12]  K. Najim,et al.  Adaptive control—practical aspects and application to a grinding circuit , 1997 .

[13]  Hai-Bin Yu,et al.  Product flow rate control in ball mill grinding process using fuzzy logic controller , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[14]  Gao Song,et al.  The mill load control for grinding plant based on fuzzy logic , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[15]  Kyung-Soo Kim,et al.  Disturbance Observer for Estimating Higher Order Disturbances in Time Series Expansion , 2010, IEEE Transactions on Automatic Control.

[16]  Eric C. Kerrigan,et al.  Robust Nonlinear Model Predictive Control of a Run-of-Mine Ore Milling Circuit , 2010, IEEE Transactions on Control Systems Technology.

[17]  Tianyou Chai,et al.  Data-Driven Optimization Control for Safety Operation of Hematite Grinding Process , 2015, IEEE Transactions on Industrial Electronics.