动态优化双估计器的多模型自适应混合控制
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摘要
针对参数子集个数较多导致计算量较大和由于系统参数发生跳变造成系统暂态性能差的问题,本文提出了基于动态优化双估计器的多模型自适应混合控制方法.首先对多个参数子集进行动态优化得到最优参数子集,减少了需要计算的模型数量,提高了系统收敛速度;其次对被控对象设置一个固定初值的估计器和一个可重新赋值的估计器,固定估计器用于初始时刻对参数的估计,可赋值估计器动态调整估计初值用于减小估计误差,提高系统暂态性能.最后的仿真结果表明了该方法的有效性,并给出了系统的稳定性及收敛性分析.
Aiming at the large number of parameter subset and the large amount of computation and transient performance due to the jump of system parameters, a multiple model adaptive mixing control method based on dynamic optimization and dual estimator is proposed in this paper. First, parameters of dynamic optimization to obtain the optimal subset of parameters, reduce the number of models of computation, improve the convergence rate of the system; secondly,set a fixed initial estimator and a reassignment estimator for the plant, with fixed estimator to the initial estimation of the parameters, you can assign the estimator of dynamic adjustment initial estimates for reducing the estimation error, improve the system transient performance. The final simulation results show the effectiveness of the proposed method, and the stability and convergence analysis of the system are given.
Aiming at the large number of parameter subset and the large amount of computation and transient performance due to the jump of system parameters, a multiple model adaptive mixing control method based on dynamic optimization and dual estimator is proposed in this paper. First, parameters of dynamic optimization to obtain the optimal subset of parameters, reduce the number of models of computation, improve the convergence rate of the system; secondly,set a fixed initial estimator and a reassignment estimator for the plant, with fixed estimator to the initial estimation of the parameters, you can assign the estimator of dynamic adjustment initial estimates for reducing the estimation error, improve the system transient performance. The final simulation results show the effectiveness of the proposed method, and the stability and convergence analysis of the system are given.
引文
[1]NARENDRA K S,BALAKRISHNAN N J.Improving transient response of adaptive control system using multiple models and switching.IEEE Transactions on Automatic Control,1994,39(9):1861-1866.
[2]NARENDRA K S,ZHOU H.New concepts control using multiple models.IEEE Transactions on Automatic Control,2012,57(1):78-89.
[3]HUANG Miao,WANG Xin,WANG Zhenlei.Multiple models adaptive control based on time series of a class of nonlinear systems.Acta Automatica Sinica,2013,39(5):581-586.(黄淼,王昕,王振雷.一类非线性系统的基于时间序列的多模型自适应控制.自动化学报,2013,39(5):581-586.)
[4]HUANG Miao,WANG Xin,WANG Zhenlei.Multiple model adaptive control for a class of nonlinear multi-variable systems with zeroorder proximity boundedness.Acta Automatica Sinica,2014,40(9):2058-2066.(黄淼,王昕,王振雷.非线性多变量零接近有界系统的多模型自适应控制.自动化学报,2014,40(9):2058-2066.)
[5]HUANG M,WANG X,WANG Z L.Multiple adaptive control for a class of linear-bounded nonlinear systems.IEEE Transactions on Automatic Control,2015,60(1):271-276.
[6]ZHANG Weicun,LIU Jiwei,HU Guangda.Stability analysis of robust multiple model adaptive control systems.Acta Automatica Sinica,2015,41(1):113-121.(张维存,刘冀伟,胡广大.鲁棒多模型自适应控制系统的稳定性.自动化学报,2015,41(1):113-121.)
[7]ZHANG Weicun.Weighted multiple model adaptive control of discrete-time stochastic system with uncertain parameters.Acta Automatica Sinica,2015,41(3):541-550.(张维存.参数不确定离散随机系统的加权多模型自适应控制.自动化学报,2015,41(3):541-550.)
[8]KUIPERS M,IOANNOU P A.Multiple model adaptive control with mixing.IEEE Transactions on Automatic Control,2010,55(8):1822-1836.
[9]KUIPERS M.Multiple model adaptive control with mixing.California:University of Southern California,2009.
[10]BALID S,IOANNOU P A.Discrete-time adaptive mixing with stability-preserving interpolation:the output regulation problem.Proceedings of the 18th IFAC World Congress.Milan:IFAC Proceedings Volumes,2011:102-107.
[11]BALDI S,IOANNOU P A.Multiple model adaptive mixing control:the discrete-time Case.IEEE Transactions on Automatic Control,2012,57(4):1040-1045.
[12]CAO Xufeng,WANG Zhenlei,WANG Xin.Multiple model adaptive mixing control based on switching.Acta Automatica Sinica,2017,43(1):94-100.(曹叙风,王振雷,王昕.基于切换机制的多模型自适应混合控制.自动化学报,2017,43(1):94-100.)
[13]BALID S,IOANNOU P A.Stability margins in adaptive mixing control via a lyapunov-based switching criterion.IEEE Transactions on Automatic Control,2016,61(5):1194-1207.
[14]NARENDRA K S,HAN Z.Location of models in multiple-model based adaptive control for improved performance.2010 American Control Conference.Baltimore:ACC,2010:117-122.
[15]GOODWIN G,SIN K.Adaptive Filtering,Prediction and Control..Englewood Cliffs:Prentice Hall,1984.
[16]QU Baida,Lü Yanhui,HUANG Xiaoyuan.Robust H∞control for discrete-time systems with nonlinear perturbations.Control Theory&Applications,2000,17(2):309-311.(屈百达,吕艳辉,黄小原.非线性摄动离散系统H∞鲁棒控制.控制理论与应用,2000,17(2):309-311.)
[17]YAESH I,SHAKED U.A transfer function approach to discrete time H∞control.IEEE Transactions on Automatic Control,1991,36(11):1264-1271.
[2]NARENDRA K S,ZHOU H.New concepts control using multiple models.IEEE Transactions on Automatic Control,2012,57(1):78-89.
[3]HUANG Miao,WANG Xin,WANG Zhenlei.Multiple models adaptive control based on time series of a class of nonlinear systems.Acta Automatica Sinica,2013,39(5):581-586.(黄淼,王昕,王振雷.一类非线性系统的基于时间序列的多模型自适应控制.自动化学报,2013,39(5):581-586.)
[4]HUANG Miao,WANG Xin,WANG Zhenlei.Multiple model adaptive control for a class of nonlinear multi-variable systems with zeroorder proximity boundedness.Acta Automatica Sinica,2014,40(9):2058-2066.(黄淼,王昕,王振雷.非线性多变量零接近有界系统的多模型自适应控制.自动化学报,2014,40(9):2058-2066.)
[5]HUANG M,WANG X,WANG Z L.Multiple adaptive control for a class of linear-bounded nonlinear systems.IEEE Transactions on Automatic Control,2015,60(1):271-276.
[6]ZHANG Weicun,LIU Jiwei,HU Guangda.Stability analysis of robust multiple model adaptive control systems.Acta Automatica Sinica,2015,41(1):113-121.(张维存,刘冀伟,胡广大.鲁棒多模型自适应控制系统的稳定性.自动化学报,2015,41(1):113-121.)
[7]ZHANG Weicun.Weighted multiple model adaptive control of discrete-time stochastic system with uncertain parameters.Acta Automatica Sinica,2015,41(3):541-550.(张维存.参数不确定离散随机系统的加权多模型自适应控制.自动化学报,2015,41(3):541-550.)
[8]KUIPERS M,IOANNOU P A.Multiple model adaptive control with mixing.IEEE Transactions on Automatic Control,2010,55(8):1822-1836.
[9]KUIPERS M.Multiple model adaptive control with mixing.California:University of Southern California,2009.
[10]BALID S,IOANNOU P A.Discrete-time adaptive mixing with stability-preserving interpolation:the output regulation problem.Proceedings of the 18th IFAC World Congress.Milan:IFAC Proceedings Volumes,2011:102-107.
[11]BALDI S,IOANNOU P A.Multiple model adaptive mixing control:the discrete-time Case.IEEE Transactions on Automatic Control,2012,57(4):1040-1045.
[12]CAO Xufeng,WANG Zhenlei,WANG Xin.Multiple model adaptive mixing control based on switching.Acta Automatica Sinica,2017,43(1):94-100.(曹叙风,王振雷,王昕.基于切换机制的多模型自适应混合控制.自动化学报,2017,43(1):94-100.)
[13]BALID S,IOANNOU P A.Stability margins in adaptive mixing control via a lyapunov-based switching criterion.IEEE Transactions on Automatic Control,2016,61(5):1194-1207.
[14]NARENDRA K S,HAN Z.Location of models in multiple-model based adaptive control for improved performance.2010 American Control Conference.Baltimore:ACC,2010:117-122.
[15]GOODWIN G,SIN K.Adaptive Filtering,Prediction and Control..Englewood Cliffs:Prentice Hall,1984.
[16]QU Baida,Lü Yanhui,HUANG Xiaoyuan.Robust H∞control for discrete-time systems with nonlinear perturbations.Control Theory&Applications,2000,17(2):309-311.(屈百达,吕艳辉,黄小原.非线性摄动离散系统H∞鲁棒控制.控制理论与应用,2000,17(2):309-311.)
[17]YAESH I,SHAKED U.A transfer function approach to discrete time H∞control.IEEE Transactions on Automatic Control,1991,36(11):1264-1271.