基于FHM的一类非线性网络控制系统的控制研究
|
摘要
针对线性网络控制系统(NCS)的研究已经日益成熟,而对非线性网络控制系统的研究还处于初级阶段,特别是很少涉及网络控制系统的干扰消除或抑制。因此,对与于非线性网络控制系统还需要更深入,同时,也期待更具创新价值的新方法。模糊双曲正切模型(FHM)是一种新的非线性状态空间模型,可以用来逼近某一类复杂非线性系统。本文建立基于FHM的一类连续非线性网络控制系统模型,考虑网络诱导时延、数据包丢失以及外界干扰的情况,设计控制器,使系统渐近稳定。值得注意的是,本文在整个控制器的设计过程中都没有限制或者规定网络诱导时延的大小,而只是要求其满足一定的条件。所以,系统的网络诱导时延可以是随机的,而且有可能大于一个采样周期。文中的控制器均是模糊双曲正切形式,容易实现。
本文首先概述了网络控制系统的研究现状及其性能指标,重点分析了NCS中主要存在的问题包括网络诱导时延和数据包丢失,介绍分析非线性系统的模糊双曲正切模型(FHM)及其性质。
其次,采用FHM逼近的一类连续非线性网络控制系统,建立NCS的模糊双曲正切模型,并采用网络化双曲正切型控制器(NHC)实现对系统的控制。NHC本身是一种模糊控制器,与传统的线性反馈控制器相比,这种非线性反馈控制器在实际应用中更合理。
然后,基于一类连续非线性网络控制系,通过选取Lyapunov-Krasovskii泛函,设计了网络化双曲正切型控制器,给出其存在的充分条件,使系统在存在网络丢包和网络诱导时延的情况下仍能保持渐近稳定。并考虑一个CSTR系统的FHM模型,并对所设计各个控制方案进行仿真,给出相应的仿真结果,结果证明了所设计控制器的有效性。
最后,针对一类连续非线性网络控制系统,考虑网络丢包和网络诱导时延的情况,提出了干扰抑制方法,基于FHM给出了状态反馈控制器的设计方法,该控制器可确保闭环系统是渐近稳定的,且具有给定的干扰抑制水平。然后考虑一个CSTR系统的FHM模糊模型,仿真验证该结论。
The study on linear networked control systems (NCS) has been increasingly mature, while it on nonlinear NCS is still in the initial stage, especially few study on nonlinear NCS has involved the external disturbance suppression. Therefore, it is necessary for more in-depth study on nonlinear NCS and more innovation designing method is in expectation. Fuzzy hyperbolic model (FHM) is a new nonlinear state space model which can be used to approximate a certain class of complex continuous-time nonlinear systems. In this dissertation,the nonlinear NCS is represented by a FHM and the controllers are designed to make sure the closed-loop system is asymptotic stability, considering network-induced delay, packet loss and external disturbance. It should be noted that, in the controller design process, network-induced delay never be restricted or specified but only just be required to meet a certain range. Therefore, the system's network-induced delay can be random, and may be greater than a sampling period. In this dissertation, all controllers are in fuzzy hyperbolic form, easy to implement.
Firstly,this dissertation briefly introduces the research development and the performance of NCS in a comparative all-round way and analyzed network-induced delay and packet loss especially, then briefly illustrate the research of FHM.
Secondly,FHM is used to estimate the states of the continuous-time nonlinear NCS in order to get a NCS model controlled by hyperbolic network controller (HNC).HNC is itself a kind of fuzzy controller and this nonlinear feedback controller is more reasonable in practical applications compared with the conventional linear feedback controller.
Thirdly, the controller design method is presented and the sufficient conditions for asymptotic stability of the closed-loop system are obtained for a class of continuous time nonlinear NCS considering network-induced delay and packet loss. Then the FHM of CSTR system simulation is implemented to prove that the controller is valid.
In the end, the disturbance attenuation control scheme is proposed and the controller design method is given based on a FHM for a class of continuous time nonlinear NCS with network-induced delay and packet loss. The controller can guarantee the closed-loop system is asymptotic stability a prescribed disturbance attenuation performance level. Then the simulation of a CSTR system based on FHM also proves that the controller is valid.
本文首先概述了网络控制系统的研究现状及其性能指标,重点分析了NCS中主要存在的问题包括网络诱导时延和数据包丢失,介绍分析非线性系统的模糊双曲正切模型(FHM)及其性质。
其次,采用FHM逼近的一类连续非线性网络控制系统,建立NCS的模糊双曲正切模型,并采用网络化双曲正切型控制器(NHC)实现对系统的控制。NHC本身是一种模糊控制器,与传统的线性反馈控制器相比,这种非线性反馈控制器在实际应用中更合理。
然后,基于一类连续非线性网络控制系,通过选取Lyapunov-Krasovskii泛函,设计了网络化双曲正切型控制器,给出其存在的充分条件,使系统在存在网络丢包和网络诱导时延的情况下仍能保持渐近稳定。并考虑一个CSTR系统的FHM模型,并对所设计各个控制方案进行仿真,给出相应的仿真结果,结果证明了所设计控制器的有效性。
最后,针对一类连续非线性网络控制系统,考虑网络丢包和网络诱导时延的情况,提出了干扰抑制方法,基于FHM给出了状态反馈控制器的设计方法,该控制器可确保闭环系统是渐近稳定的,且具有给定的干扰抑制水平。然后考虑一个CSTR系统的FHM模糊模型,仿真验证该结论。
The study on linear networked control systems (NCS) has been increasingly mature, while it on nonlinear NCS is still in the initial stage, especially few study on nonlinear NCS has involved the external disturbance suppression. Therefore, it is necessary for more in-depth study on nonlinear NCS and more innovation designing method is in expectation. Fuzzy hyperbolic model (FHM) is a new nonlinear state space model which can be used to approximate a certain class of complex continuous-time nonlinear systems. In this dissertation,the nonlinear NCS is represented by a FHM and the controllers are designed to make sure the closed-loop system is asymptotic stability, considering network-induced delay, packet loss and external disturbance. It should be noted that, in the controller design process, network-induced delay never be restricted or specified but only just be required to meet a certain range. Therefore, the system's network-induced delay can be random, and may be greater than a sampling period. In this dissertation, all controllers are in fuzzy hyperbolic form, easy to implement.
Firstly,this dissertation briefly introduces the research development and the performance of NCS in a comparative all-round way and analyzed network-induced delay and packet loss especially, then briefly illustrate the research of FHM.
Secondly,FHM is used to estimate the states of the continuous-time nonlinear NCS in order to get a NCS model controlled by hyperbolic network controller (HNC).HNC is itself a kind of fuzzy controller and this nonlinear feedback controller is more reasonable in practical applications compared with the conventional linear feedback controller.
Thirdly, the controller design method is presented and the sufficient conditions for asymptotic stability of the closed-loop system are obtained for a class of continuous time nonlinear NCS considering network-induced delay and packet loss. Then the FHM of CSTR system simulation is implemented to prove that the controller is valid.
In the end, the disturbance attenuation control scheme is proposed and the controller design method is given based on a FHM for a class of continuous time nonlinear NCS with network-induced delay and packet loss. The controller can guarantee the closed-loop system is asymptotic stability a prescribed disturbance attenuation performance level. Then the simulation of a CSTR system based on FHM also proves that the controller is valid.
引文
[1] Haleviv Y,Ray A. Integrated conmunition and control systems:part I-analysis. ASME Journal of Dynamic Systems,Maesurement,and Control,1988,110(4):367-373.
[2] Ray A,Haleviv Y. Integrated conmunition and control systems:part 2-design considerations. ASME Journal of Dynamic Systems,Maesurement,and Control,1988,110(4):374-381
[3]张庆灵,张雪峰.网络控制系统研究综述与前景展望.信息与控制,2007,36(3):364~370
[4]吴晨,许哲,何婧,许化龙.网络控制系统建模与控制.电光与控制,2009,16(9):37~39
[5]张士超,仪垂杰,郭健翔,林海波,刘尊民,周扬名,方玲.集散控制系统的发展及应用现状.微计算机信息,2007,23(1-1):94~96
[6]杨诗国,网络系统面临的发展与挑战.安庆师范学院学报,2008,14(3):104~106
[7]张霞.现场总线控制系统及其应用.黑龙江纺织. 2009,6(2):19~20
[8]姚琳.工业以太网概述及方案简介.2009,8:78~81
[9] NILSSON J. Real-time control systems with delays [D]. Lund Institute of Technology,1998
[10] LIAN Fengli. Analysis design modeling and control of networked control systems[D]. Michigan:The University of Michigan,2001
[11]袁国强.一类基于切换的非线性网络控制系统的控制研究.北京:中国地质大学(北京)硕士论文,2010
[12]于水情,李俊民.具有输出延时的网络化切换系统的稳定性分析。西安电子科技大学学报,2005,32(5):478-452,806
[13]卢建宁,赵光宙.离散时滞切换系统稳定性分析.信息与控制,2005,34(3):381-384
[14]毛红保,徐卿,梁伟.基于Web的远程故障诊断技术研究.计算机工程与设计,2005,26(3):686~687,749
[15] Zhang W,Bran icky M S,Phillips S M. Stability of networked control systems. IEEE Control Systems Magazine,2001,21(1):84~99
[16] Liu CL,a,d Layland J,W. Schudling Algorithm for multiprogramming in a hard-real-time encironment. Journal of the Association for Computing Machinery,1973,20(1):46-61
[17] Walsh G C,Ye H,Bushnell L. Stability analysis of network control systems.Proc Amer Control Conf San Diego,CA,1999. 2876-2880
[18]Walsh G C,Ye H. Seheduling of networked control systems. IEEE Control system Magazine,2001,21(1):57-65
[19]王跃飞,王成红.基于网络控制的若干基本问题的思考和分析.自动化学报,2002,28(增刊):171-176
[20]杨丽曼,李运华,袁海斌.网络控制系统的时延特性分析及数据传输技术研究。控制与决策,2004,19(4):361-367
[21]白涛,吴智铭,杨根科.网络化的控制系统.控制理论与应用,2004,21(4):584-590
[22] Rabello A , Bhaya A. Stability of asynchronous dynamical systems with rate constraints and application.IEEE proceedings Control Theory Application.2003,150(5):546-550
[23]张化光,全永兵.模糊双曲正切模型的建模方法与控制器设计.自动化学报,2000,26(6):729-735
[24] Zhang H G,Quan Y B. Modeling,identification and control of a class of nonlinear system. IEEE Transactions on Fuzzy Systems,2001,9(2):349-354
[25]全永兵,张化光.一类非线性系统的建模、辨识与控制研究.控制理论与应用,2001
[26] CohenB,SaadD,Mzrom E.Efficient training of recurrent neural network with delays. Neural Networks,1997,10(1):51-59
[27] Zhang H G,Li M,Yang J,Yang D. Fuzzy model-based robust networked control for a Class of nonlinear systems. IEEE Transactions on systems,Man,and Cybernetics,Part A:Systems and Humans,2009,:39(2):437-447
[28]李丽,刘晓东.一类不确定时延网络控制系统的模糊控制.系统工程与电子技术,2009,31(1):146-152
[29]全永兵.非线性系统的模糊建模与控制方法研究.沈阳:东北大学博士论文,2001
[30] Zhang H G,Wang Z,Chaotifying fuzzy hyperbolic model using adaptibe inverse optional control approach. International Journal of Bifurcation and Chaos,2004,14(10):3505-3517
[31] Kim D S,Lee Y S,Kwon W H,et. al. Maximum allowable delay bounds of networked control system. Control Engineering Practice,2003,11(11):1301-1313
[32] Zheng Y,Fang H,Wang H O. Takagi-Sugeno fuzzy-model-based fault detection for networked control system with Markov delay. IEEE Transactions on Systems,Man and Cybernetics,Part B:Cybernetics,2006,36(4):924-929
[33]杨珺.非线性系统的模糊控制与网络控制研究[D].东北大学博士论文,2008
[34] Mastiellone S,Abdallash C T,Dorato P. Model-based networked control for nonlinear systems with stochastic packet dropout. Proceeding of a American Control Conference,Portland,2005,2365-2370
[35] Nesic D,Teel A R. Input-state stability of networked control system. Automatic,2004,40(12):2121-2128
[36] Nesic D,Teel A R. Input-output stability properties of networked control system. IEEE Transactions on Automatic Control,2004,49(10):1650-1667
[37] Walsh G C,Beldiman O,Bushnell L G. Asympoptic behavior of nonlinear networked control systems. IEEE Transactions on Automatic Control.,2001,46(7):1093-1097
[38] Yue D,Han Q L,Lam J. Networked-based robust control of systems with uncertainty. Automatic,2005,41(6):999-1007
[39] Seiler P,Sengupta R.An H∞approach for networked control. IEEE Transactions on Automatic Control,2005,50(3):356-364
[40]吴晨,许哲,何婧.网络控制系统建模与控制.光电与控制,2009,16(9):37-39,43
[41] Hale J,Lunel S M V. Introduction to Functional Differential Equations. New York:Springer-Verlag,1993
[42] Bliman P A. LMI characterization of the strong delay-independent stability of liner delay systems via quadratic Lyapunov-Krasovskii functional. Systems Control Letters,2001,43(1):263~274
[43] Nuclescu S I. Delay Efficts on Stability. A Robust Control Approach. London:Springer-Verlag,2001
[44] Boyd S,Ghauoi L E,et. al. Linear Matrix Inequalities in Systems,1996,4(1):14-23
[45]俞立.鲁棒控制—线性矩阵不等式处理方法[M].北京:清华大学出版社,2002
[46] Lee Y S,Moon Y S,Kwon W H,Park P G. Delay-dependent robust H∞control for uncertain systems with a state-delay. Automatica,2004,40:65-72
[47] Apkarian P,Tuan H D,Bernussou J. Continuous-time analysis,eigenstructure assignment ,and synthesis with enhanced linear matrix inequalities (LMI) characterizations. IEEE Transactions on Automatic Control,2001,46(12):1941-1946
[48] Gao H,Chen T,Lam J. A new delay systems approach to network-based control. Automatic,2008,44(1):39-52
[49] He Y,Wang Q G,Li C,et al. Delay-ranged-dependent stability for systems with time varying-delay. Automayic,2007,43(2):371-376
[50]张化光.模糊双曲正切模型[M].北京:科学出版社,2009
[51] hang H G,Yang J,Su C Y. T-S fuzzy-model-based robust H∞design for network control systems with uncertainties. IEEE Transactions on Industrial Informatics,2007,3(4):289~301
[2] Ray A,Haleviv Y. Integrated conmunition and control systems:part 2-design considerations. ASME Journal of Dynamic Systems,Maesurement,and Control,1988,110(4):374-381
[3]张庆灵,张雪峰.网络控制系统研究综述与前景展望.信息与控制,2007,36(3):364~370
[4]吴晨,许哲,何婧,许化龙.网络控制系统建模与控制.电光与控制,2009,16(9):37~39
[5]张士超,仪垂杰,郭健翔,林海波,刘尊民,周扬名,方玲.集散控制系统的发展及应用现状.微计算机信息,2007,23(1-1):94~96
[6]杨诗国,网络系统面临的发展与挑战.安庆师范学院学报,2008,14(3):104~106
[7]张霞.现场总线控制系统及其应用.黑龙江纺织. 2009,6(2):19~20
[8]姚琳.工业以太网概述及方案简介.2009,8:78~81
[9] NILSSON J. Real-time control systems with delays [D]. Lund Institute of Technology,1998
[10] LIAN Fengli. Analysis design modeling and control of networked control systems[D]. Michigan:The University of Michigan,2001
[11]袁国强.一类基于切换的非线性网络控制系统的控制研究.北京:中国地质大学(北京)硕士论文,2010
[12]于水情,李俊民.具有输出延时的网络化切换系统的稳定性分析。西安电子科技大学学报,2005,32(5):478-452,806
[13]卢建宁,赵光宙.离散时滞切换系统稳定性分析.信息与控制,2005,34(3):381-384
[14]毛红保,徐卿,梁伟.基于Web的远程故障诊断技术研究.计算机工程与设计,2005,26(3):686~687,749
[15] Zhang W,Bran icky M S,Phillips S M. Stability of networked control systems. IEEE Control Systems Magazine,2001,21(1):84~99
[16] Liu CL,a,d Layland J,W. Schudling Algorithm for multiprogramming in a hard-real-time encironment. Journal of the Association for Computing Machinery,1973,20(1):46-61
[17] Walsh G C,Ye H,Bushnell L. Stability analysis of network control systems.Proc Amer Control Conf San Diego,CA,1999. 2876-2880
[18]Walsh G C,Ye H. Seheduling of networked control systems. IEEE Control system Magazine,2001,21(1):57-65
[19]王跃飞,王成红.基于网络控制的若干基本问题的思考和分析.自动化学报,2002,28(增刊):171-176
[20]杨丽曼,李运华,袁海斌.网络控制系统的时延特性分析及数据传输技术研究。控制与决策,2004,19(4):361-367
[21]白涛,吴智铭,杨根科.网络化的控制系统.控制理论与应用,2004,21(4):584-590
[22] Rabello A , Bhaya A. Stability of asynchronous dynamical systems with rate constraints and application.IEEE proceedings Control Theory Application.2003,150(5):546-550
[23]张化光,全永兵.模糊双曲正切模型的建模方法与控制器设计.自动化学报,2000,26(6):729-735
[24] Zhang H G,Quan Y B. Modeling,identification and control of a class of nonlinear system. IEEE Transactions on Fuzzy Systems,2001,9(2):349-354
[25]全永兵,张化光.一类非线性系统的建模、辨识与控制研究.控制理论与应用,2001
[26] CohenB,SaadD,Mzrom E.Efficient training of recurrent neural network with delays. Neural Networks,1997,10(1):51-59
[27] Zhang H G,Li M,Yang J,Yang D. Fuzzy model-based robust networked control for a Class of nonlinear systems. IEEE Transactions on systems,Man,and Cybernetics,Part A:Systems and Humans,2009,:39(2):437-447
[28]李丽,刘晓东.一类不确定时延网络控制系统的模糊控制.系统工程与电子技术,2009,31(1):146-152
[29]全永兵.非线性系统的模糊建模与控制方法研究.沈阳:东北大学博士论文,2001
[30] Zhang H G,Wang Z,Chaotifying fuzzy hyperbolic model using adaptibe inverse optional control approach. International Journal of Bifurcation and Chaos,2004,14(10):3505-3517
[31] Kim D S,Lee Y S,Kwon W H,et. al. Maximum allowable delay bounds of networked control system. Control Engineering Practice,2003,11(11):1301-1313
[32] Zheng Y,Fang H,Wang H O. Takagi-Sugeno fuzzy-model-based fault detection for networked control system with Markov delay. IEEE Transactions on Systems,Man and Cybernetics,Part B:Cybernetics,2006,36(4):924-929
[33]杨珺.非线性系统的模糊控制与网络控制研究[D].东北大学博士论文,2008
[34] Mastiellone S,Abdallash C T,Dorato P. Model-based networked control for nonlinear systems with stochastic packet dropout. Proceeding of a American Control Conference,Portland,2005,2365-2370
[35] Nesic D,Teel A R. Input-state stability of networked control system. Automatic,2004,40(12):2121-2128
[36] Nesic D,Teel A R. Input-output stability properties of networked control system. IEEE Transactions on Automatic Control,2004,49(10):1650-1667
[37] Walsh G C,Beldiman O,Bushnell L G. Asympoptic behavior of nonlinear networked control systems. IEEE Transactions on Automatic Control.,2001,46(7):1093-1097
[38] Yue D,Han Q L,Lam J. Networked-based robust control of systems with uncertainty. Automatic,2005,41(6):999-1007
[39] Seiler P,Sengupta R.An H∞approach for networked control. IEEE Transactions on Automatic Control,2005,50(3):356-364
[40]吴晨,许哲,何婧.网络控制系统建模与控制.光电与控制,2009,16(9):37-39,43
[41] Hale J,Lunel S M V. Introduction to Functional Differential Equations. New York:Springer-Verlag,1993
[42] Bliman P A. LMI characterization of the strong delay-independent stability of liner delay systems via quadratic Lyapunov-Krasovskii functional. Systems Control Letters,2001,43(1):263~274
[43] Nuclescu S I. Delay Efficts on Stability. A Robust Control Approach. London:Springer-Verlag,2001
[44] Boyd S,Ghauoi L E,et. al. Linear Matrix Inequalities in Systems,1996,4(1):14-23
[45]俞立.鲁棒控制—线性矩阵不等式处理方法[M].北京:清华大学出版社,2002
[46] Lee Y S,Moon Y S,Kwon W H,Park P G. Delay-dependent robust H∞control for uncertain systems with a state-delay. Automatica,2004,40:65-72
[47] Apkarian P,Tuan H D,Bernussou J. Continuous-time analysis,eigenstructure assignment ,and synthesis with enhanced linear matrix inequalities (LMI) characterizations. IEEE Transactions on Automatic Control,2001,46(12):1941-1946
[48] Gao H,Chen T,Lam J. A new delay systems approach to network-based control. Automatic,2008,44(1):39-52
[49] He Y,Wang Q G,Li C,et al. Delay-ranged-dependent stability for systems with time varying-delay. Automayic,2007,43(2):371-376
[50]张化光.模糊双曲正切模型[M].北京:科学出版社,2009
[51] hang H G,Yang J,Su C Y. T-S fuzzy-model-based robust H∞design for network control systems with uncertainties. IEEE Transactions on Industrial Informatics,2007,3(4):289~301