基于Hammerstein-Wiener模型的广义预测控制
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摘要
提出了一种新型的基于Hammerstein-Wiener模型的广义预测控制策略。采用基于最小二乘支持向量机的Hammerstein-Wiener模型描述非线性系统动态特性,作为被控对象预测模型。同时,针对现有遗传算法和混沌粒子群优化算法收敛速度慢和精度低等缺点,给出一种拟牛顿信赖域混沌粒子群混合优化算法,作为预测控制的滚动优化策略,函数测试和非线性对象的广义预测控制的滚动优化表明该算法的优越性。最后,对设计的预测控制器进行实例仿真,结果表明它能满足系统实时稳定运行的需求,取得了良好的控制效果。
A novel generalized predictive control(GPC)strategy based on the Hammerstein-Wiener model is proposed.The dynamic characteristics of the nonlinear system are described by the Hammerstein-Wiener model based on the support vector machine,so a prediction model of the controlled object is obtained.Furthermore,an optimization algorithm of chaotic particle swarm combined with quasi-Newton trust region(QN-TR)is proposed in order to avoid the deficiency of slow convergence speed and low accuracy of the genetic algorithm and the chaotic particle swarm optimization(CPSO)algorithm,so a rolling optimization strategy of the predictive control is obtained.Function tests and rolling optimization of the GPC to the nonlinear object reflect the superiority of the algorithm.Finally,the results of the simulation example for the generalized predictive controller show that it can meet the demand of real-time and stable operation of the system,and a good control effect is obtained.
A novel generalized predictive control(GPC)strategy based on the Hammerstein-Wiener model is proposed.The dynamic characteristics of the nonlinear system are described by the Hammerstein-Wiener model based on the support vector machine,so a prediction model of the controlled object is obtained.Furthermore,an optimization algorithm of chaotic particle swarm combined with quasi-Newton trust region(QN-TR)is proposed in order to avoid the deficiency of slow convergence speed and low accuracy of the genetic algorithm and the chaotic particle swarm optimization(CPSO)algorithm,so a rolling optimization strategy of the predictive control is obtained.Function tests and rolling optimization of the GPC to the nonlinear object reflect the superiority of the algorithm.Finally,the results of the simulation example for the generalized predictive controller show that it can meet the demand of real-time and stable operation of the system,and a good control effect is obtained.
引文
[1]Clarke D W,Mohtadi C,Tuffs P S.Generalized predictive control[J].Automatica,1987,23(2):137-148.
[2]Yuan Z D.Generalized predictive control based on ARMAX model[J].Control Theory&Applications,1988,5(1):12-17.(袁震东.基于ARMAX模型的广义预侧控制[J].控制理论与应用,1988,5(1):12-17.)
[3]Akpan V A,Hassapis G D.Nonlinear model identification and adaptive model predictive control using neural networks[J].Trans.on ISA,2011,50(2):177-194.
[4]Wang S X,Dong C,Liu H R.Generalized nonlinear predictive controller based on T-S fuzzy model and small-world optimization algorithm[J].Control and Decision,2011,26(5):673-678.(王爽心,董旸,刘海瑞.基于T-S模型和小世界优化算法的广义非线性预测控制[J].控制与决策,2011,26(5):673-678.)
[5]Zhang S T,Bai S Z.Controller design of uncertain nonlinear systems based on T-S fuzzy model[J].Control Theory&Applications,2009,7(2):139-143.
[6]Zhu Y C.Estimation of an N-L-N Hammerstein-Wiener model[J].Automatica,2002,38:1067-1614.
[7]Wills A,Schn T B,Ljung L,et al.Identification of Hammerstein-Wiener models[J].Automatica,2013,49(1):70-81.
[8]Ding B,Huang B.Output feedback model predictive control for nonlinear systems represented by Hammerstein-Wiener model[J].Control Theory&Applications,2007,1(5):1302-1310.
[9]Bloemen H H J,Van Den Boom T J J,Verbruggen H B.Modelbased predictive control for Hammerstein-Wiener systems[J].International Journal of Control,2001,74(5):482-495.
[10]Tong Z N,Xiao L,Peng K X,et,al.Constrained generalized predictive control of mould level based on genetic algorithm[J].Control and Decision,2009,24(11):1735-1739.(童朝南,肖磊,彭开香,等.一基于遗传算法的结晶器液位约束广义预测控制[J].控制与决策,2009,24(11):1735-1739.)
[11]Xiao B X,Zhu Z G,Liu Y F.Research of hybrid optimized generalized predictive controller based on particle swarm optimization[J].Journal of System Simulation,2007,19(4):820-824.(肖本贤,朱志国,刘一福.基于粒子群算法混合优化的广义预测控制器研究[J].系统仿真学报,2007,19(4):820-824.)
[12]Wang Z H,Sun Y X.Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints[C]∥Proc.of the Second International Conference on Computational Intelligence and Natural Computing,2010:303-306.
[13]Zou Q Y,Ji J W,Ma L L.Greenhouse air temperature predictive control using the particle swarm optimisation algorithm[C]∥Proc.of the 4th International Symposium on Intelligent Information Technology in Agriculture,2007:495-499.
[14]Jiang H M,Kwong C K,Chen Z Q,et al.Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control[J].Knowledge-Based Systems,2012,39(1):194-201.
[15]Huynh D C,Dunnigan M W,Finney S J.Energy efficient control of an induction machine using a chaos particle swarm optimization algorithm[C]∥Proc.of the IEEE International Conference on Power and Energy,2010:450-455.
[16]Gertz E M.A quasi-Newton trust-region method[J].Mathematical Programming,2004,100(3):447-470.
[17]Gui W H,Song H Y,Yang C H.Hammerstein-Wiener model identified by least-square-support-vector machine and its application[J].Control Theory&Applications,2008,25(3):393-397.(桂卫华,宋海鹰,阳春华.Hammerstein-Wiener模型最小二乘向量机辨识及其应用[J].控制理论与应用,2008,25(3):393-397.)
[18]Liu J M,Gao Y L.Chaos particle swarm optimization algorithm[J].Computer Applications,2008,28(2):322-325.(刘军民,高岳林.混沌粒子群优化算法[J].计算机应用,2008,28(2):322-325.)
[19]Liu L,Qian W M,Qian F.An Improved Chaos-Particle Swarm Optimization Algorithm[J].Journal of East China University of Science and Technology(Natural Science Edition),2010,36(2):267-272.(刘玲,钟伟民,钱锋.改进的混沌粒子群优化算法[J].华东理工大学学报(自然科学版),2010,36(2):267-272.)
[20]Sun W Y,Yuan Y X.Optimization theory and methods:nonlinear programming[M].New York:Springer-Verlag,2006:637-647.
[2]Yuan Z D.Generalized predictive control based on ARMAX model[J].Control Theory&Applications,1988,5(1):12-17.(袁震东.基于ARMAX模型的广义预侧控制[J].控制理论与应用,1988,5(1):12-17.)
[3]Akpan V A,Hassapis G D.Nonlinear model identification and adaptive model predictive control using neural networks[J].Trans.on ISA,2011,50(2):177-194.
[4]Wang S X,Dong C,Liu H R.Generalized nonlinear predictive controller based on T-S fuzzy model and small-world optimization algorithm[J].Control and Decision,2011,26(5):673-678.(王爽心,董旸,刘海瑞.基于T-S模型和小世界优化算法的广义非线性预测控制[J].控制与决策,2011,26(5):673-678.)
[5]Zhang S T,Bai S Z.Controller design of uncertain nonlinear systems based on T-S fuzzy model[J].Control Theory&Applications,2009,7(2):139-143.
[6]Zhu Y C.Estimation of an N-L-N Hammerstein-Wiener model[J].Automatica,2002,38:1067-1614.
[7]Wills A,Schn T B,Ljung L,et al.Identification of Hammerstein-Wiener models[J].Automatica,2013,49(1):70-81.
[8]Ding B,Huang B.Output feedback model predictive control for nonlinear systems represented by Hammerstein-Wiener model[J].Control Theory&Applications,2007,1(5):1302-1310.
[9]Bloemen H H J,Van Den Boom T J J,Verbruggen H B.Modelbased predictive control for Hammerstein-Wiener systems[J].International Journal of Control,2001,74(5):482-495.
[10]Tong Z N,Xiao L,Peng K X,et,al.Constrained generalized predictive control of mould level based on genetic algorithm[J].Control and Decision,2009,24(11):1735-1739.(童朝南,肖磊,彭开香,等.一基于遗传算法的结晶器液位约束广义预测控制[J].控制与决策,2009,24(11):1735-1739.)
[11]Xiao B X,Zhu Z G,Liu Y F.Research of hybrid optimized generalized predictive controller based on particle swarm optimization[J].Journal of System Simulation,2007,19(4):820-824.(肖本贤,朱志国,刘一福.基于粒子群算法混合优化的广义预测控制器研究[J].系统仿真学报,2007,19(4):820-824.)
[12]Wang Z H,Sun Y X.Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints[C]∥Proc.of the Second International Conference on Computational Intelligence and Natural Computing,2010:303-306.
[13]Zou Q Y,Ji J W,Ma L L.Greenhouse air temperature predictive control using the particle swarm optimisation algorithm[C]∥Proc.of the 4th International Symposium on Intelligent Information Technology in Agriculture,2007:495-499.
[14]Jiang H M,Kwong C K,Chen Z Q,et al.Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control[J].Knowledge-Based Systems,2012,39(1):194-201.
[15]Huynh D C,Dunnigan M W,Finney S J.Energy efficient control of an induction machine using a chaos particle swarm optimization algorithm[C]∥Proc.of the IEEE International Conference on Power and Energy,2010:450-455.
[16]Gertz E M.A quasi-Newton trust-region method[J].Mathematical Programming,2004,100(3):447-470.
[17]Gui W H,Song H Y,Yang C H.Hammerstein-Wiener model identified by least-square-support-vector machine and its application[J].Control Theory&Applications,2008,25(3):393-397.(桂卫华,宋海鹰,阳春华.Hammerstein-Wiener模型最小二乘向量机辨识及其应用[J].控制理论与应用,2008,25(3):393-397.)
[18]Liu J M,Gao Y L.Chaos particle swarm optimization algorithm[J].Computer Applications,2008,28(2):322-325.(刘军民,高岳林.混沌粒子群优化算法[J].计算机应用,2008,28(2):322-325.)
[19]Liu L,Qian W M,Qian F.An Improved Chaos-Particle Swarm Optimization Algorithm[J].Journal of East China University of Science and Technology(Natural Science Edition),2010,36(2):267-272.(刘玲,钟伟民,钱锋.改进的混沌粒子群优化算法[J].华东理工大学学报(自然科学版),2010,36(2):267-272.)
[20]Sun W Y,Yuan Y X.Optimization theory and methods:nonlinear programming[M].New York:Springer-Verlag,2006:637-647.