DOI: 10.3724/SP.J.1219.2013.00723

Information and Control (信息与控制) 2013/42:6 PP.723-728

Online Support Vector Regression Predictive Control Algorithm Based on Particle Swarm Optimization

For the problems of model mismatch and difficulty in solving objective function in the predictive control of the nonlinear system model, an online support vector regression predictive control algorithm based on particle swarm optimization (PSO) is proposed. An nonlinear predictive model for the object is built based on the online support vector regression, and the object is identified and the identified model also can be self-adjusted through online learning. Meanwhile, the objective function is solved by PSO, the rolling optimization is realized. The nonlinear system simulation results show the effectiveness and adaptability of the presented algorithm.

Key words:nonlinear model predictive control,online support vector regression,particle swarm optimization (PSO),rolling optimization

ReleaseDate:2015-04-15 18:52:42

[1] 舒迪前.预测控制系统及其应用[M].北京:机械工业出版,1996: 1-36. Shu D Q. Predictive control system and its application[M]. Beijing: China Machine Press, 1996: 1-36.

applications to variational inequalities[M]. Berlin, Germany:Springer-Verlag, 2009: 20-35.

[2] 席裕庚.预测控制[M].北京:国防工业出版社,1993:5-18. Xi Y G. Predictive control[M]. Beijing: National Defense Industry Press, 1993: 5-18.

[3] Qin S J, Badgwell T A. A survey of industrial model predictive control technology[J]. Control Engineering Practice, 2003, 11(7): 733-764.

[4] Hosen M A, Hussain M A, Mjalli F S. Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation[J]. Control Engineering Practice, 2011, 19(5): 454-467.

[5] Han H G, Qiao J F, Chen Q L. Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network[J]. Control Engineering Practice, 2012, 20(4): 465-476.

[6] Zhong W, Pi D. Support vector machine based nonlinear model multi-step-ahead optimizing predictive control[J]. Journal of Central South University of Technology, 2005, 12(5): 591-595.

[7] 郭振凯,宋召青,毛剑琴.基于最小二乘支持向量机的非线性广义预测控制[J].控制与决策,2009,24(4):520-525. Guo Z K, Song Z Q, Mao J Q. Nonlinear generalized predictive control based on least square support vector machine[J]. Control and Decision, 2009, 24(4): 520-525.

[8] Shin J, Jin Kim H, Kim Y. Adaptive support vector regression for UAV flight control[J]. Neural Networks, 2011, 24(1): 109-120.

[9] Vapnik V N. The nature of statistical learning theory[M]. New York, USA: Springer, 1999: 23-103.

[10] Liu Y, Chen W, Wang H, et al. Adaptive control of nonlinear time-varying processes using selective recursive kernel learning method[J]. Industrial & Engineering Chemistry Research, 2011, 50(5): 2773-2780.

[11] Ma J, James T, Simon P. Accurate on-line support vector regression[J]. Neural Computation, 2003, 15(11): 2683-2704.

[12] Noriega J R, Wang H. A direct adaptive neural-network control for unknown nonlinear systems and its application[J]. IEEE Transactions on Neural Networks, 1998, 9(1): 27-34.

[13] 王寅,荣冈,王树青.基于T-S模糊模型的非线性预测控制策略[J].控制理论与应用,2002,19(4):599-603.Wang Y, Rong G, Wang S Q. Nonlinear predictive control strategybased on T-S fuzzy model[J]. Control Theory and Applications, 2002,19(4): 599-603.

[14] Dostál Z. Optimal quadratic programming algorithms:With

[15] Verma H, Jain C, Rathore A, et al. A comparative study of GA, PSO andBig Bang-Big Crunch optimization techniques for optimal placement ofSVC's[J]. International Journal of Electronics Communication andComputer Engineering, 2012, 3(3): 263-269.

[16] 穆朝絮,张瑞民,孙长银.基于粒子群优化的非线性系统最小二乘支持向量机预测控制方法[J].控制理论与应用,2010,27(2):164-168.Mu Z X, Zhang R M, Sun C Y. LS-SVM predictive control based on PSOfor nonlinear systems[J]. Control Theory and Applications, 2010,27(2): 164-168.