基于DSP的永磁同步电机神经网络逆解耦控制的研究
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摘要
永磁同步电机是当今众多高精度、高效、节能自动化控制系统的驱动机构的核心执行部件。对永磁同步电机的控制策略进行改良可以直接改善这些自动化设备的工作性能,达到提高产品性能,节能降耗的目的。本文针对矢量控制等依赖于对象精确模型的永磁同步电机高性能控制方法中存在的问题(如只能实现稳态解耦,鲁棒性和适应性差等),将神经网络与逆系统方法结合,提出了永磁同步电机的神经网络逆解耦控制方法。该方法能减小电机参数时变、负载扰动以及未建模动态对电机解耦控制的影响,真正实现永磁同步电机的线性化动态解耦控制,有效地提高永磁同步电机的控制性能。论文主要进行了以下的研究工作:
首先,应用非线性系统的线性代数方法,给出了一般非线性系统的静态逆系统和动态逆系统的构造算法,说明了非线性系统可逆性的充要条件以及伪线性系统复合构造的方法,从理论上阐述了逆系统的线性化解耦控制策略。
然后,全面、系统地分析了永磁同步电机的可逆性及其逆系统的线性化解耦特征,给出了永磁同步电机的状态反馈结合输入积分逆系统的解析表达式,并通过仿真实验分析了参数变化及负载扰动对解析逆解耦控制产生的严重影响。得出了永磁同步电机的解析逆系统方法在实际中难以应用的结论。
为解决由于永磁同步电机因受参数变化和负载扰动影响而使解析逆系统方法难以在实际中应用的难题,提出了永磁同步电机的神经网络逆解耦控制方法。给出了永磁同步电机神经网络逆模型的结构形式、辨识方法及实现步骤,将永磁同步电机这样一个多变量、强耦合、时变的非线性对象动态解耦成两个SISO的伪线性积分子系统。之后,进一步对两个积分子系统分别设计闭环线性控制器,使整个系统具有很强的参数鲁棒性和良好的抗负载扰动的能力,为逆系统方法在高性能永磁同步电机控制中的应用莫定了基础。
最后,在以IPM为功率变换核心,DSP为数字化控制单元的电机控制实验平台上实现了永磁同步电机神经网络逆解耦控制实验。控制系统具有良好的动、静态性能。实验结果表明永磁同步电机的神经网络逆解耦控制方法是一种有效和实用的高性能控制方法。
Permanent Magnet Synchronous Motors(PMSM) are core parts of many high precision,high efficiency and low power automatic control systems. Improving on the strategies of PMSM control can enhance the performances of those automatic systems directly.This dissertation focuses on some problems in decoupling control strategies,such as that only can realize static decoupling,run short of both the robustness on parameter variation and the ability to resist load disturbance.These strategies depend on Permanent Magnet Synchronous Motor (PMSM) models.Combine neural networks with inverse system method,the neural network inverse system method of Permanent Magnet Synchronous Motor control is proposed.The Permanent Magnet Synchronous Motor,which is a multi-variable,strongly coupling and nonlinear object,is linearised and decoupled into two SISO subsystems.The influence caused by parameter variation and load disturbance decrease evidently.This method provides a new approach for high performance control of Permanent Magnet Synchronous Motor. Main progresses in this dissertation are as follows:
First,the decoupling control theory based on inverse system method is studied by using the linear algebraic method of nonlinear systems.The necessary and sufficient conditions for invertibility of nonlinear system are developed,and the construction algorithms for pseudo-linear subsystems are given respectively.
Secondly,the invertibility and decoupling property of Permanent Magnet Synchronous Motor are analyzed systematically and thoroughly.The structures of the state feedback combined with the input integral inverse system,which achieve linearization and decoupling of Permanent Magnet Synchronous Motor, are put forward.The influence of motor parameter variation and load disturbance to the decoupling control performance is discussed,which shows that the analytical inverse method is not able to achieve the high performance for Permanent Magnet Synchronous Motor control.
In order to eliminate the influence resulted from parameter variation and load disturbance,a neural network inverse system method is proposed to realize the decoupling control of Permanent Magnet Synchronous Motor.The structure of neural network inverse system,the identification approach and realization steps to obtain the neural network inversion are given.After the neural network inversion is connected before the Permanent Magnet Synchronous Motor in series,the Permanent Magnet Synchronous Motor is decoupled into two SISO pseudo-linear integral subsystems.Then,close-loop linear controllers are designed.The control system has good robustness to parameter variation and strong adaptability to load disturbance.
Finally,the proposed method is validated through a controlling experimental platform which is based on digital signal processor(DSP) and intelligent power module(IPM).The control performance is satisfying,which shows that the neural network inverse system method is an effective and applicative method for the control of Permanent Magnet Synchronous Motor.
首先,应用非线性系统的线性代数方法,给出了一般非线性系统的静态逆系统和动态逆系统的构造算法,说明了非线性系统可逆性的充要条件以及伪线性系统复合构造的方法,从理论上阐述了逆系统的线性化解耦控制策略。
然后,全面、系统地分析了永磁同步电机的可逆性及其逆系统的线性化解耦特征,给出了永磁同步电机的状态反馈结合输入积分逆系统的解析表达式,并通过仿真实验分析了参数变化及负载扰动对解析逆解耦控制产生的严重影响。得出了永磁同步电机的解析逆系统方法在实际中难以应用的结论。
为解决由于永磁同步电机因受参数变化和负载扰动影响而使解析逆系统方法难以在实际中应用的难题,提出了永磁同步电机的神经网络逆解耦控制方法。给出了永磁同步电机神经网络逆模型的结构形式、辨识方法及实现步骤,将永磁同步电机这样一个多变量、强耦合、时变的非线性对象动态解耦成两个SISO的伪线性积分子系统。之后,进一步对两个积分子系统分别设计闭环线性控制器,使整个系统具有很强的参数鲁棒性和良好的抗负载扰动的能力,为逆系统方法在高性能永磁同步电机控制中的应用莫定了基础。
最后,在以IPM为功率变换核心,DSP为数字化控制单元的电机控制实验平台上实现了永磁同步电机神经网络逆解耦控制实验。控制系统具有良好的动、静态性能。实验结果表明永磁同步电机的神经网络逆解耦控制方法是一种有效和实用的高性能控制方法。
Permanent Magnet Synchronous Motors(PMSM) are core parts of many high precision,high efficiency and low power automatic control systems. Improving on the strategies of PMSM control can enhance the performances of those automatic systems directly.This dissertation focuses on some problems in decoupling control strategies,such as that only can realize static decoupling,run short of both the robustness on parameter variation and the ability to resist load disturbance.These strategies depend on Permanent Magnet Synchronous Motor (PMSM) models.Combine neural networks with inverse system method,the neural network inverse system method of Permanent Magnet Synchronous Motor control is proposed.The Permanent Magnet Synchronous Motor,which is a multi-variable,strongly coupling and nonlinear object,is linearised and decoupled into two SISO subsystems.The influence caused by parameter variation and load disturbance decrease evidently.This method provides a new approach for high performance control of Permanent Magnet Synchronous Motor. Main progresses in this dissertation are as follows:
First,the decoupling control theory based on inverse system method is studied by using the linear algebraic method of nonlinear systems.The necessary and sufficient conditions for invertibility of nonlinear system are developed,and the construction algorithms for pseudo-linear subsystems are given respectively.
Secondly,the invertibility and decoupling property of Permanent Magnet Synchronous Motor are analyzed systematically and thoroughly.The structures of the state feedback combined with the input integral inverse system,which achieve linearization and decoupling of Permanent Magnet Synchronous Motor, are put forward.The influence of motor parameter variation and load disturbance to the decoupling control performance is discussed,which shows that the analytical inverse method is not able to achieve the high performance for Permanent Magnet Synchronous Motor control.
In order to eliminate the influence resulted from parameter variation and load disturbance,a neural network inverse system method is proposed to realize the decoupling control of Permanent Magnet Synchronous Motor.The structure of neural network inverse system,the identification approach and realization steps to obtain the neural network inversion are given.After the neural network inversion is connected before the Permanent Magnet Synchronous Motor in series,the Permanent Magnet Synchronous Motor is decoupled into two SISO pseudo-linear integral subsystems.Then,close-loop linear controllers are designed.The control system has good robustness to parameter variation and strong adaptability to load disturbance.
Finally,the proposed method is validated through a controlling experimental platform which is based on digital signal processor(DSP) and intelligent power module(IPM).The control performance is satisfying,which shows that the neural network inverse system method is an effective and applicative method for the control of Permanent Magnet Synchronous Motor.
引文
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[4]Yu H,Jayabalan R,Fahimi B.Field oriented linear induction motor drivers[J].Electromagnetic field Computation 2006 12~(th) Biennial IEEE conference,pp:282-284.
[5]Jiri Simanek,Jaroslav Novak,Ondrej Cemy,et al.FOC and Flux Weakening for Traction Drive with Permanent Magnet Synchronous Motor[J].IEEE Transactions on Industrial Electronics,2008,pp:753-758.
[6]F.Tahami,H.Nademi,M.Rezaei.A High-Performance Vector-Controlled PMSM Drive with Maximum Torque per Ampere Operation[C].2nd IEEE International Conference on Power and Energy(PECon 08),December 1-3,2008,pp:254-258.
[7]Soliman H,Elbuluk M E.Improving the torque ripple in DTC of PMSM using fuzzy logic[J].IEEE Trans.on IA.,2008,5-9 Oct:1-8.
[8]Ortega Carlos,Arias Antoni,Balcells Josep.High frequency injection in a matrix convertor DTC drive for sensorless operation of a PMSM[J].IEEE Trans.on IS.,2007,4-7 June,pp:2278-2283.
[9]刘军,刘丁,吴浦升,等.基于模糊控制调节电压矢量作用时间策略的永磁同步电机直接转矩控制仿真研究[J].中国电机工程学报,2004,24(10):148-152.
[10]Medagam P.V,Yucelen T,Pourtoqhrat F.Adaptive SDRE-Based nonlinear sensorless speed control for PMSM drives[C].Power Symposium,2007,39~(th) North American,Sept 30-Oct 2,pp:518-522.
[11]Zhaojun Meng,Zhangzhi Sun,Yunjue An.Chaos anti-control of permanent magnet synchronous motor based on model matching[C].Eletronical Machines and Systems,2007,International Conference,8-11 Oct,pp:1748-1752.
[12]Fanjin Sun,Xinxiang Pan,Yancheng Liu.The design of position estimator for PMSM by using diagonal recurrent neural network[J].IEEE International Conference on AL,2007,18-21 Aug,pp:2084-2088.
[13]张建民,王科俊.永磁同步电机的模糊混沌神经网络建模[J].中国电机工程学报,2007,27(3):7-11.
[14]Gong Shu-qiu,Ding xi-ying,Wu Wei.Application of a self-tuning two degree of freedom PID controller based on fuzzy inference for PMSM[J],IEEE.International Conference on EMS,17-20,Oct,2008,pp:1629-1632.
[15]Pajchrowski T,Zawirski K.Application of fuzzy logic techniques to robust speed control of PMSM[J],IEEE.Trans,EPE-PEMC,1-3 Sept,2008,pp:1198-1203.
[16]YingHong Qi,Xibin Cao.Research on autonomous rendezvous using dynamically focused fuzzy learning control[C],IEEE.Trans.WCICA,The 6~(th) World Congress on,2006,vol(2),pp:8505-8508.
[17]Rajesh Kumar,Gupta R A,Bansal A K.Identification and Control of PMSM using artificial neural network[J],IEEE Trans Industrial Electronics,4-7 June,2007,pp:30-35.
[18]Wei Chen,Yaonan Wang.Neural network adaptive L2 robust control for induction motor[J],IEEE Trans,Natural Computation 4~(th) International Conference on,vol.3,18-20Oct,2008,pp:474-479.
[19]Keyong Shap,Hongyu Gao,Xianli Yu.Neural network variable structure control of nonlinear time-delay systems based on robust control[C].IEEE Trans,Intelligent Control and Automation,The 6~(th) World Congress on,vol.1,2006,pp:2461-2464.
[20]Jianming Lian,Yonggon Lee,Sudhoff S D,Zak S H.Variable structure neural network based direct adaptive robust control of uncertain systems[J].IEEE Trans.American Control Conference,11-13 June,2008,pp:3402-3407.
[21]Kocalmis A,Sunter S.Application of a neural network based space vector PWM algorithm to multi-level inverters[J].IEEE Trans.Electronical Machines and Power Electronics,2007,pp:181-185.
[22]高为炳.非线性控制系统导论.北京:科学出版社,1988.
[23]冯纯伯.非线性控制系统分析与设计.南京:东南大学出版社,1990.
[24]冯纯伯.非线性系统的鲁棒性控制.北京:科学出版社,2004.
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