基于模糊神经网络逆系统的无轴承永磁同步电机解耦控制
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摘要
无轴承永磁同步电机是一个多变量、非线性和强耦合的系统,因此实现转矩和径向悬浮力的动态解耦是无轴承永磁同步电机实现稳定高速高精度运行的关键。提出一种基于模糊神经网络逆系统的新型解耦控制方法,介绍了无轴承永磁同步电机基本结构和工作原理基础,并建立无轴承永磁同步电机转矩和悬浮力的数学模型。在对数学模型进行可逆性分析的基础上,采用模糊神经网络构建一个有效的逆系统,通过将逆系统与原系统串联,使原非线性系统解耦为3个单输入–单输出子系统,并同时设计了闭环控制器。对所设计的控制系统进行仿真和实验研究。仿真和实验结果表明,这种解耦控制方法可以实现无轴承永磁同步电机转矩和悬浮力之间的解耦控制,并具有良好的动态性能和稳定性。
A bearingless permanent magnet synchronous motor(BPMSM) is a multi-variable, nonlinear and strong coupling system, so it is necessary to realize dynamic decoupling control among torque and suspension forces for the stable, high speed and high precision operation of the BPMSM.In this paper, a novel decoupling control method based on the inverse system using the adaptive neuro-fuzzy inference system(ANFIS) was proposed. Firstly, the basic structure and working principle of the BPMSM were introduced, and the mathematical models of the suspension forces and torque were established. Secondly, based on the reversibility analysis of the mathematical models, the ANFIS was applied to build an effective inverse system of the BPMSM. Thirdly, by connecting the inverse system in series with the original system,the nonlinear original system was pseudo-linearized into three single input single output(SISO) subsystems, and additional closed-loop controllers were designed. Finally, the simulations and experiments were carried out. The results show that, the proposed control method can realize the decoupling control among torque and suspension forces of the BPMSM and it has good dynamic performance and stability.
A bearingless permanent magnet synchronous motor(BPMSM) is a multi-variable, nonlinear and strong coupling system, so it is necessary to realize dynamic decoupling control among torque and suspension forces for the stable, high speed and high precision operation of the BPMSM.In this paper, a novel decoupling control method based on the inverse system using the adaptive neuro-fuzzy inference system(ANFIS) was proposed. Firstly, the basic structure and working principle of the BPMSM were introduced, and the mathematical models of the suspension forces and torque were established. Secondly, based on the reversibility analysis of the mathematical models, the ANFIS was applied to build an effective inverse system of the BPMSM. Thirdly, by connecting the inverse system in series with the original system,the nonlinear original system was pseudo-linearized into three single input single output(SISO) subsystems, and additional closed-loop controllers were designed. Finally, the simulations and experiments were carried out. The results show that, the proposed control method can realize the decoupling control among torque and suspension forces of the BPMSM and it has good dynamic performance and stability.
引文
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[12]Navaneethakkannan C,Sudha M.Analysis and implementation of ANFIS-based rotor position controller for BLDC motors[J].Journal of Power Electronics,2016,16(2):564-571.
[13]Erdogan H,Ozdemir M.Neuro-fuzzy approach on core resistance estimation at loss minimization control of permanent magnet synchronous motor[J].Electronics and Electrical Engineering,2016,22(3):7-12.
[14]Zeng L,Zhang X H.Internal model control of inductive magnetic suspension spherical active joints based on fuzzy neural network inverse system[J].Advances in Mechanical Engineering,2015,7(11):1-11.
[15]Chao C,Robert J,Jamie T,et al.An extended ANFISarchitecture and its learning properties for type-1 and interval type-2 models[C]//Proceeding of IEEEInternational Conference on Fuzzy Systems.Vancouver,Canada,2016:602-609.
[16]Omar F L,Samsul B M,Mohammad H M.A genetically trained simplified ANFIS controller to control nonlinear MIMO systems[C]//Proceeding of International Conference on Electrical,Control and Computer Engineering.Kuantan,Malaysia,2011:349-354.
[17]Van S N,Dinh K L,Ming M H.ANFIS controller for an active magnetic bearing system[C]//Proceeding of IEEEInternational Conference on Fuzzy Systems,Hyderabad,India,2013.
[18]李国勇,杨丽娟.神经模糊预测控制及其MATLAB实现[M].北京:电子工业出版社,2016:264-267.Li Guoyong,Yang Lijuan.Neuro fuzzy predictive control and its MATLAB implementation[M].Beijing:Publishing House of Electronics Industry,2016:264-267(in Chinese).
[2]Reichert T,Nussbaumer T,Kolar J W.Investigation of exterior rotor bearingless motor topologies for high-quality mixing applications[J].IEEE Transactions and Industry Applications,2012,48(6):2206-2216.
[3]田拥胜,孙岩桦,虞烈.高速永磁电机电磁轴承转子系统的动力学及实验研究[J].中国电机工程学报,2012,32(9):116-123.Tian Yongsheng,Sun Yanhua,Yu Lie.Dynamical and experimental researches of active magnetic bearing rotor systems for high-speed PM machines[J].Proceedings of the CSEE,2012,32(9):116-123(in Chinese).
[4]孙晓东,朱熀秋,杨泽斌.无轴承永磁同步电机技术综述及其发展趋势探讨[J].中国机械工程,2012,23(17):2128-2135.Sun Xiaodong,Zhu Huangqiu,Yang Zebing.An overview and development trend of bearingless permanent magnet synchronous motors[J].China Mechanical Engineering,2012,23(17):2128-2135(in Chinese).
[5]Zhang S R,Luo F L.Direct control of radial displacement for bearingless permanent-magnet-type synchronous motors[J].IEEE Transactions on Industrial Electronics,2009,56(2):542-552.
[6]邓智泉,仇志坚,王晓琳,等.无轴承永磁同步电的转子定向控制研究[J].中国电机工程学报,2005,25(1):104-108.Deng Zhiquan,Chou Zhijian,Wang Xiaolin,et al.Study on rotor flux orientation control of permanent magnet bearingless synchronous motors[J].Proceedings of the CSEE,2005,25(1):104-108(in Chinese).
[7]Sun X D,Shi Z,Chen L,et al.Internal model control for a bearingless permanent magnet synchronous motor based on inverse system method[J].IEEE Transactions on Energy Conversion,2016,31(4):1539-1548.
[8]孙玉坤,费德成,朱熀秋.基于α阶逆系统五自由度无轴承永磁同步电机解耦控制[J].中国电机工程学报,2006,26(1):120-126.Sun Yukun,Fei Decheng,Zhu Huangqiu.Study on decoupling control of the 5 degree-of-freedom bearingless permanent magnet motor based onα-th order inverse system method[J].Proceedings of the CSEE,2006,26(1):120-126(in Chinese).
[9]张少如,吴爱国,王利军,等.无轴承永磁同步电机的神经网络逆控制[J].中国机械工程,2008,19(22):2681-2693.Zhang Shaoru,Wu Aiguo,Wang Lijun,et al.Neural network inverse control of bearingless permanent magnettype synchronous motors[J].China Mechanical Engineering,2008,19(22):2681-2693(in Chinese).
[10]Wang Q,Xu B G,You L R,et al.BPMSM control using a sliding mode controller with fractional order of suspension forces based on differential geometry[J].International Journal of Control and Automation,2015,8(6):187-198.
[11]Sekhar J N C,Marutheswar G V.Direct torque control of induction motor using enhanced firefly algorithmANFIS[J].Journal of Circuits Systems and Computers,2017,26(6):1-24.
[12]Navaneethakkannan C,Sudha M.Analysis and implementation of ANFIS-based rotor position controller for BLDC motors[J].Journal of Power Electronics,2016,16(2):564-571.
[13]Erdogan H,Ozdemir M.Neuro-fuzzy approach on core resistance estimation at loss minimization control of permanent magnet synchronous motor[J].Electronics and Electrical Engineering,2016,22(3):7-12.
[14]Zeng L,Zhang X H.Internal model control of inductive magnetic suspension spherical active joints based on fuzzy neural network inverse system[J].Advances in Mechanical Engineering,2015,7(11):1-11.
[15]Chao C,Robert J,Jamie T,et al.An extended ANFISarchitecture and its learning properties for type-1 and interval type-2 models[C]//Proceeding of IEEEInternational Conference on Fuzzy Systems.Vancouver,Canada,2016:602-609.
[16]Omar F L,Samsul B M,Mohammad H M.A genetically trained simplified ANFIS controller to control nonlinear MIMO systems[C]//Proceeding of International Conference on Electrical,Control and Computer Engineering.Kuantan,Malaysia,2011:349-354.
[17]Van S N,Dinh K L,Ming M H.ANFIS controller for an active magnetic bearing system[C]//Proceeding of IEEEInternational Conference on Fuzzy Systems,Hyderabad,India,2013.
[18]李国勇,杨丽娟.神经模糊预测控制及其MATLAB实现[M].北京:电子工业出版社,2016:264-267.Li Guoyong,Yang Lijuan.Neuro fuzzy predictive control and its MATLAB implementation[M].Beijing:Publishing House of Electronics Industry,2016:264-267(in Chinese).