Flatness intelligent control via improved least squares support vector regression algorithm

Abstract

To overcome the disadvantage that the standard least squares support vector regression (LS-SVR) algorithm is not suitable to multiple-input multiple-output (MIMO) system modelling directly, an improved LS-SVR algorithm which was defined as multi-output least squares support vector regression (MLSSVR) was put forward by adding samples’ absolute errors in objective function and applied to flatness intelligent control. To solve the poor-precision problem of the control scheme based on effective matrix in flatness control, the predictive control was introduced into the control system and the effective matrix-predictive flatness control method was proposed by combining the merits of the two methods. Simulation experiment was conducted on 900HC reversible cold roll. The performance of effective matrix method and the effective matrix-predictive control method were compared, and the results demonstrate the validity of the effective matrix-predictive control method.

Keywords

least squares support vector regression / multi-output least squares support vector regression / flatness / effective matrix; predictive control

References

[1] ZhangX-w, WangYan. Application and development trend of intelligent recognition methods for flatness recognition [J]. Journal of Iron and Steel Research, 2010, 22(1): 1-3

[2] LiuH-m, HeH-t, ShanX-ying. Flatness control based on dynamic effective matrix for cold strip mills [J]. Chinese Journal of Mechanical Engineering, 2009, 22(2): 287-296

[3] LiuH-m, JiaC-y, ShanX-ying. Application of intelligent methods in flatness control [J]. Journal of Yanshan University, 2010, 34(1): 1-5

[4] LiuJ, WangY-q, SunFu. Fuzzy pattern recognition method of flatness based on particle swarm theory [J]. Chinese Journal of Mechanical Engineering, 2008, 44(1): 173-178

[5] LiL-juanThe study of modeling and predictive control design based on least squares support vector machine [D], 2008HangzhouZhejiang University, Control Science and Engineering Department80-81

[6] SuykensJ A K, VandewalleJ. Least squares support vector machine classifiers [J]. Neural Processing Letter, 1999, 9(3): 293-300

[7] HuC-h, CaiY-ning. Simultaneous fault diagnosis based on multi-regression LS-SVM [J]. J of Huazhong Univ of Sci & Tech: Natural Science Edition, 2009, 37(1): 1-5

[8] SuykensJ A K, van GestelTLeast squares support vector machines [M], 2002SingaporeWorld Scientific Publishing Co Pte Ltd

[9] ZhangX-l, PangZ-p, LiS-qing. Flatness pattern recognition based on adaptive neuro-fuzzy inference system [J]. Journal of Iron and Steel Research, 2009, 21(9): 59-62

[10] YinG-f, WangY-q, SunX-guang. Research on shape signal pattern recognition based on neural network [J]. China Mechanical Engineering, 2004, 15(24): 15-18

[11] RenH-p, LiuD, ZhengGang. A kinetic algorithm approach to shape pattern recognition [J]. Heavy Machinery, 2002, 3: 9-12

[12] ZhangX-l, LiuH-min. GA-BP Model of flatness pattern recognition and improved Least-squares method [J]. Iron and steel, 2003, 38(10): 29-34

[13] LiuJ-c, ChenY-y, ZhangR-you. Intelligent flatness-controller based on PSO-BP network [J]. Control Theory & Applications, 2007, 24(4): 674-678

[14] HeH-t, LiNan. The improved rbf network approach to flatness pattern recognition based on SVM [J]. Process Automation Instrumentation, 2007, 28(5): 1-4

[15] ZhangX-lingResearch on intelligent control and recognition of flatness for cold strip mill [D], 2002QinhuangdaoYanshan University, Institute of Electrical Engineering20-99

[16] ShanX-y, LiuH-min. A recognition method of new flatness pattern containing the cubic flatness [J]. Iron and Steel, 2010, 45(8): 56-60

[17] HeH-t, LiNan. The improved RBF network approach to flatness pattern recognition based on SVM [J]. Process Automation Instrumentation, 2007, 28(5): 1-4

[18] JiaoWei. Overview of intelligent algorithms in nonlinear model predictive control [J]. Journal of System Simulation, 2008, 20(24): 6581-6586

[19] BaoZ-jin. Application of support vector machines in intelligent modeling and model predictive control [D]. Hangzhou: Zhejiang University, Control Science and Engineering Department, 20074-12