Data-Based Research on Predictive Control of Multiple Mismatched Model Parameters

Abstract:

The basis of the paper is that there are already some methods to accurately evaluate, test and diagnose the performance of the model predictive controller. And the result shows the reason of a bad performance of control system is because of model mismatch. There are much more complexity and variety in the problem of multiple mismatched parameters than single mismatched parameter, so we need consider more factors about it on the basis of the solution of single mismatched parameter. We propose a way of adjusting model parameters based on fuzzy rules when there are more than one mismatched parameters. The method is to adjust the step-size of parameters and get the adjustment rules on the basis of the changes of maximum overshoot, rising time and settling time. The last, verifying the method is effective by experiments.

• Periodical: Applied Mechanics and Materials (Volumes 397-400)
• Pages: 1331-1336
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.397-400.1331
• Online since: September 2013
• Authors: Da Kuo He*, Jun Qi Xin, Wen Long Yuan, Qing Yun Yuan
• Keywords: Fuzzy Rules, Model-Plant Mismatch, Multi-Parameter, Predictive Control

References

[1] Zheng Zhou: Survey of Current Progress in BP Neural Network. Shanxi Electronic Technology. Vol. 32 (2008), pp.90-92.

[2] Yiqiu Qian, Jia Tian: A Tool Wear Predictive Model Based on SVM. 2010 Chinese Control and Decision Conference. (2010), pp.1213-1217.

DOI: 10.1109/ccdc.2010.5498161

[3] Yonglei Zhao: Research on Flatness Forecasting Model and Generalize Predictive Control Algorithm for Cold Mill. Yanshan University. (2010).

[4] Jixiang Jin: The Coal Seam Thickness Prediction Method Research Based on Seismic Attributes' Multiple Regression Analysis. Taiyuan University of Technology. (2010).

[5] Hong Chen, Xiaojun Geng: Recent Advances in Research on Predictive Control Performance. Control Theory and Application. Vol. 41(2009), pp.74-75.

[6] Ming Zhang, Shifu Wang: Predicted-Error-Based Model Algorithm Control. Journal of Tsinghua University. Vol. 30(1990), pp.90-95.

[7] Qiaolin Yuan, Barry Lennox: Control performance assessment for multivariable systems based on a modified relative variance technique. Journal of Process Control. Vol. 19(2009), pp.489-497.

DOI: 10.1016/j.jprocont.2008.05.005

[8] Junsheng Jiao, in: The Research of Multivariable System Control Performance Assessment Based on Generalized Minimum Variance. Beijing University of Chemical Technology (2012).

[9] Gaoxian Wu, in: Generalized Minimum Variance Self-tuning Reset PID Controller and its Application in the Pressure System. Central South University (2012).

[10] Biao Huang, Steven X. Ding: Practical solutions to multivariate feedback control performance assessment problem reduced a priori knowledge of interactor matrices. Journal of Process Control. Vol. 15(2005), pp.573-583.

DOI: 10.1016/j.jprocont.2004.10.006

[11] Byung-Su Ko, Thomas F. Edgar: PID control performance assessment: the single loop case. American Institute of Chemical Engineering. Vol. 50(2004), pp.1211-1218.

DOI: 10.1002/aic.10104

[12] Ziyu Lin, Yi Jiang: A New Algorithm on Lagged Correlation Analysis Between Time Series: TPFP. Journal of Computer Research and Development. Vol. 49(2012), pp.2645-2655.

[13] Jiangbo Qian, Zhijie Wang: Correlation Analysis on Multidimensional Data Streams Based on Base-Windows. PR&AI. Vol. 25(2012), pp.435-444.

[14] Min Han, Ru Wei: Correlation analysis and prediction of multivariate time series based on phase space synchronization. Systems Engineering and Electronics. Vol. 32(2010), pp.2426-2430.

[15] He Dakuo, Shao Shuai, Yang Pingyu, Zhang Shuning: Research on diagnosis method of predictive control performance model based on data. Lecture Notes in Computer Science. Vol. 7368(2012), pp.468-477.

DOI: 10.1007/978-3-642-31362-2_52