基于Smith预估器的无刷直流电机控制
|
摘要
无刷直流电动机用电子换向代替机械换向,以其优良的特性成为运动控制研究领域的热点。但是无刷直流电机系统存在一些固有的时间滞后因素,这些因素会对精度、快速性要求较高的系统产生不利影响,使得系统的稳定性、动态质量和调节品质下降。常规PID控制方法超调较大而且调节时间较长,在上述高性能应用中的控制效果不能令人满意。为此,本文提出利用Smith预估器来补偿无刷直流电机系统时滞环节。Smith预估器是在负反馈控制的基础上引入一个预估补偿环节,从而使系统的闭环特征环节不含有纯滞后项,消除了滞后环节对系统稳定的影响,改善了系统反应速度和动态性能。传统的Smith预估控制器需要准确并且稳定的数学模型,其性能对模型参数的误差十分敏感,在参数误差较大的情况下容易引起振荡和发散。
针对这一点,本文运用模糊-PI控制器对Smith预估器进行改进。模糊控制的优点是不需要精确的数学模型且具有较好的鲁棒性,而PI控制器能够有效的消除稳态误差,因此将模糊控制与PI控制结合起来可以有效地消除Smith预估器与实际控制对象之间的偏差。改进后的Smith预估器模型具有较强的自适应能力,得到较好的控制品质,与实际控制对象有良好的匹配性,能够确保系统状态的稳定。由于克服了传统Smith预估器需要精确模型的缺点,整个系统具有良好的鲁棒性和参数自适应性。通过仿真研究表明运用改进后的Smith预估器具有较强的参数适应性,保持时滞系统稳定具有良好的控制效果。
此外,本文利用模糊免疫控制器对传统的Smith预估器进行改进,仿真结果证明了这种方法的有效性,并具有响应速度快,抗扰能力和鲁棒性强等优点。
The brushless DC motor (BLDCM) has been broadly applied because of its eminent performance. But there are some inherent time lag factors in Brushless DC motor system. These factors have an adverse impact on the system which needs a high degree of accuracy and rapidity. They probably decrease the stability, the quality of regulation and the dynamic quality of the system. There will be greater overshoot and longer adjustment time if PID control is used only. In this paper, Smith predictor is introduced in the brushless DC motor system to compensate the time delay. Smith's principle is well known as an effective time compensator for the time delay. The main advantage of this technique is that the time delay is eliminated from the characteristic equation of the closed-loop system. Thus, the pure delay system can be transformed to the one without delay. Thus eliminates the adverse impact of due to time lag to system stability. The control system of BLDCM based on the microprocessor MSP430F149, which has the ability to realize real-time control, is proposed in this paper. Then the simulation results approve the validity.
However, an accurate and stable mathematical model is needed for traditional Smith predictor to get satisfactory control quality. If not, it may lead to divergence and oscillation of system easily. In this paper a modified Smith predictor is proposed, which use fuzzy-PI controller to eliminate the parameters’deviations between Smith predictor and actual control object rapidly. So it can ensure that the state of the system stability. This scheme enhances robustness of the controlled system and alleviates the disadvantage of traditional Smith predictor in which an exactly precise model is needed. Simulation results show that the proposed method has good parametric adaptability and can provide higher characteristics to the system of with time-varying parameters and time lag.
Secondly, another advanced Smith predictor system is proposed in this paper. The system uses fuzzy immune controller to eliminate the parameters’deviations between Smith predictor and actual control object. The simulation results approve the validity, and the new control system shows quick response speed, strong immunity ability, and strong robustness.
针对这一点,本文运用模糊-PI控制器对Smith预估器进行改进。模糊控制的优点是不需要精确的数学模型且具有较好的鲁棒性,而PI控制器能够有效的消除稳态误差,因此将模糊控制与PI控制结合起来可以有效地消除Smith预估器与实际控制对象之间的偏差。改进后的Smith预估器模型具有较强的自适应能力,得到较好的控制品质,与实际控制对象有良好的匹配性,能够确保系统状态的稳定。由于克服了传统Smith预估器需要精确模型的缺点,整个系统具有良好的鲁棒性和参数自适应性。通过仿真研究表明运用改进后的Smith预估器具有较强的参数适应性,保持时滞系统稳定具有良好的控制效果。
此外,本文利用模糊免疫控制器对传统的Smith预估器进行改进,仿真结果证明了这种方法的有效性,并具有响应速度快,抗扰能力和鲁棒性强等优点。
The brushless DC motor (BLDCM) has been broadly applied because of its eminent performance. But there are some inherent time lag factors in Brushless DC motor system. These factors have an adverse impact on the system which needs a high degree of accuracy and rapidity. They probably decrease the stability, the quality of regulation and the dynamic quality of the system. There will be greater overshoot and longer adjustment time if PID control is used only. In this paper, Smith predictor is introduced in the brushless DC motor system to compensate the time delay. Smith's principle is well known as an effective time compensator for the time delay. The main advantage of this technique is that the time delay is eliminated from the characteristic equation of the closed-loop system. Thus, the pure delay system can be transformed to the one without delay. Thus eliminates the adverse impact of due to time lag to system stability. The control system of BLDCM based on the microprocessor MSP430F149, which has the ability to realize real-time control, is proposed in this paper. Then the simulation results approve the validity.
However, an accurate and stable mathematical model is needed for traditional Smith predictor to get satisfactory control quality. If not, it may lead to divergence and oscillation of system easily. In this paper a modified Smith predictor is proposed, which use fuzzy-PI controller to eliminate the parameters’deviations between Smith predictor and actual control object rapidly. So it can ensure that the state of the system stability. This scheme enhances robustness of the controlled system and alleviates the disadvantage of traditional Smith predictor in which an exactly precise model is needed. Simulation results show that the proposed method has good parametric adaptability and can provide higher characteristics to the system of with time-varying parameters and time lag.
Secondly, another advanced Smith predictor system is proposed in this paper. The system uses fuzzy immune controller to eliminate the parameters’deviations between Smith predictor and actual control object. The simulation results approve the validity, and the new control system shows quick response speed, strong immunity ability, and strong robustness.
引文
[1]张琛.直流无刷电动机原理及应用.北京:机械工业出版社,1996
[2]叶金虎,徐思海,等.无刷直流电动机.北京:科学出版社,1982
[3]沈建新.永磁无刷直流电动机特殊绕组结构及无位置传感器控制的研究:[博士学位论文].杭州:浙江大学,1997
[4]刘文.微机控制的无位置传感器无刷直流电机及其调速系统:[硕士学位论文].西安:西安交通大学,1995
[5]王丰饶.滑模变结构控制.北京:机械工业出版社,1995
[6]夏长亮,祁温雅,杨荣,等.基于RBF神经网络的超声波电机参数辨识与模型参考自适应控制.中国电机工程学报,2004,24(7):18-22
[7]田亮.模糊控制神经网络的无刷直流电机直接转矩控制系统研究:[硕士学位论文].天津:天津大学,2006
[8]王娟.基于RBF的无刷直流电机直接电流控制与转矩分析:[硕士学位论文].天津:天津大学,2003
[9]夏长亮,郭培健,史婷娜,等.基于模糊遗传算法的无刷直流电机自适应控制.中国电机工程学报,2005,25(11):129-133
[10]孙增圻.智能控制理论与技术.清华大学出版社,广西科学技术出版社,1997
[11]纪志成,沈艳霞,薛花.无刷直流电机自适应模糊控制的研究.中国电机工程学报,2005,25[5]:104-109
[12]刘启明.模糊滑模变结构控制设计.复旦学报:自然科学版,2004,43(5):952-955
[13]夏长亮,王娟,史婷娜,等.基于自适应径向基函数神经网络的无刷直流电机直接电流控制.中国电机工程学报,2003,23(6):123-12
[14]陈炜.无刷直流电机卡尔曼法转子位置检测及模型参考自适应控制:[硕士学位论文].天津;天津大学,2003
[15]张明君,张化光.遗传算法优化的BP神经网络PID控制器.电气自动化,2005,27(6):3-5
[16]李红利,张晓彤,兰立柱,等.基于遗传算法的RBF神经网络的优化设计方法.计算机仿真,2003,20(11):67-69
[17]石红瑞,刘勇,刘宝坤,等.基于混合递阶遗传算法的径向基神经网络学习算法及其应用.控制理论与应用,2002(8)
[18]郑丕谔,马艳华.RBF神经网络的递阶遗传训练新方法.控制与决策,2003(3)
[19]李方方,赵英凯,许必熙.基于递阶遗传算法优化神经网络的研究.机械与电子,2006(2)
[20]李正军.基于自抗扰控制器的无刷直流电机控制:[硕士学位论文].天津:天津大学,2004
[21]韩京清.非线性PID控制器[J].自动化学报,1994,20(4),487-490
[22]苏玉鑫,段宝岩.一种新型非线性PID控制器[J].控制与决策,2003,18(1),26-128
[23] K.Watanbe,M.Ito.A process-model control for linear systems with delay [J].IEEE Trans.Automat.Contr,1981,26(6),1261-1269
[24] M.R.Matausek,A.D.Micic.A modified Smith predictor for controlling a process with an integrator and long dead-time[J].IEEEE Transactions on Automatic Control,1994,39(2),343-345
[25] M R Matausek,A D Micic.A modified Smith Predictor for controlling a process with an integrator and long dead-time[J].IEEE Transactions on Automatic Control,1996,41(8),1199-1203
[26] M.R.Matausek,A D.Micic.On the modified Smith predictor for controlling a process with an integrator and long dead-time[J].IEEE Transactions on Automatic Control,1999,44(8),1603-1606
[27] S.Majhi,D P.Atherton.A new Smith predictor and controller for unstable and integrating processes with time delay[J].Proceeding of the IEEE conference on Control and decision,Tampa, USA,1998,1341-1345
[28] S Majhi,D P Atherton.Obtaining controller parameters for a new Smith predictor using autotuning[J].Automatica,2000,36,1651-1658
[29] S.Majhi,D P Atherton.Autotuning and controller design for processes with small time delays[J].IEE Proceedings of the Control Theory and Application,1999,146(5),415-425
[30] Ibrahim Kaya.Obtaining controller parameters for a new PI-PD Smith predictor using autotuning[J].Journal of Process Control,2003,13,465-472
[31]张卫东,孙优贤.一类Smith预估器及其鲁棒整定[J].自动化学报,1997,23(5),660~663
[32] TLiu Y Z.Cai,D Y Gu,W.D.Zhang.New modified Smith predictor scheme for integrating and unstable processes with time delay[J].IEE Proc-Control Theory Appl,2005,152(2):238-246
[33] Zhang W D,Sun Y X, Xu X M.Two degree-of-freedom Smith predictor for processes with time delay [J],Automation,1998,34(10):1279-1282
[34] Kaya I.IMC based automatic tuning method for PID controllers in a Smith predictor configuration[J].Computers and Chemical Eng,2004,28(3):281-290
[35] Nunez-Reyes A,Normey-Rico J E,Bordons C,et.A Smith predictive based MPC in a solar air conditioning plant[J].Journal of Process control,2005,15(1):1-10
[36] Tian Y C,Gao F R.Compensation of dominant and variable delay in process systems[J]. Ind Eng Chen Res,1998,37(3):982-986.
[37] Wang Q G,Guo X,Zhang Y.Direct identification of continuous time delaysystems from step responses[J].Journal of Process Control,2001,11(5):531-542.
[38]张国范,朱晓萍,包新华.一种分析和设计Smith预估器的新方法及其应用[J].控制与决策,2001,16(3):341-343.
[39] C.E.Garcia,M Morari.Internal model control[M].Ind Eng Chen Proc Des.Dev. 1982
[40]张井岗,刘志远.一类自横对象的二自由度PID控制[J].控制与决策,2002,17(6),886~889
[41]汤莉莉,刘开培.基于IMC的不稳定时滞过程PID控制[J] .武汉大学学报,2003,36(5),118-122
[42] Changliang Xia,Junhua Liu,Wei Yu, et al.Variable structure control of BLDCM based on extended state observer.Mechatronics and Automation,2005 IEEE International Conference Volume 2,2005:568-571
[43] Fengtai Huang,Dapeng Tien.A.Neural network approach to position sensorless control of brushless DC motors.Proc.IEEE IECON 22nd International Conference,1996,2:1167-1170
[44] Chang-Liang Xia,Wei Chen.Senseless control of brushless DC motors at low speed using neural networks . Machine Learning and Cybernetics ,2005.Proceedings of 2005 International Conference on Volume 2, 18-21 Aug.2005:1099-1103
[45]陈炜.无刷直流电机卡尔曼法转子位置检测及模型参考自适应控制:[硕士学位论文].天津:天津大学,2003
[46]夏长亮,祁温雅,杨荣,等.基于混合递阶遗传算法和RBF神经网络的超声波电动机自适应速度控制.电工技术学报,2004(9)
[47]夏长亮,祁温雅,杨荣,等.基于RBF神经网络的超声波电机参数辨识与模型参考自适应控制.中国电机工程学报,2004,24(7):18-22
[48] S Yilmaz, K Howard,T David A.Direct model reference adaptive control of permanent magnet brushless DC motors . IEEE Conference on Control Applications-Proceedings,1997.9:633-638
[49] K Kyeong-Hwa,Y Myung-Joong.DSP-based high-speed sensorless control for a brushless DC motor using a DC link voltage.Electric Power Components and Systems,2002,30(9):889-906
[50] S Tomonobu,T Mutuwo,D Shinji,et al.Sensorless vector control of brushless DC motors using disturbance observer.PESC Record-IEEE Annual Power Electronics Specialists Conference,1995,2:772-777
[51]孟繁荣,李一凡,龙三平.基于TMS320F2812的永磁同步电机调速系统设计.工业技术,2006(17):95-96
[52]贺益康.交流电机调速系统计算机仿真.浙江大学出版社,1993
[53] Changliang Xia,Junhua Liu,Wei Yu,et al.Variable structure control of BLDCM based on extended state observer.Mechatronics and Automation,2005 IEEEInternational Conference Volume 2,2005:568-571
[54]胡大可.MSP430系列FLASH型超低功耗16位单片机.北京:北京航空航天大学出版社,2001
[55]张唏,王德银,张晨.MSP430系列单片机实用C语言程序设计.北京:人民邮电出版社,2005
[56]宋鹏.基于DSP的盘式无铁心永磁同步电机直接转矩控制调速系统:[硕士学位论文].天津:天津大学,2006
[57]李志强.基于RBF神经网络在线辨识的永磁无刷直流电机单神经元PID自适应控制:[硕士学位论文].天津:天津大学,2006
[58] Matsui,Nobuyuki,Takeshita,et al.Novel starting method of sensorless salient-pole brushless motor.Conference Record– IAS Annual Meeting,1994,1:386-392
[59]刘文.微机控制的无位置传感器无刷直流电机及其调速系统:[硕士学位论文],西安:西安交通大学,1995
[2]叶金虎,徐思海,等.无刷直流电动机.北京:科学出版社,1982
[3]沈建新.永磁无刷直流电动机特殊绕组结构及无位置传感器控制的研究:[博士学位论文].杭州:浙江大学,1997
[4]刘文.微机控制的无位置传感器无刷直流电机及其调速系统:[硕士学位论文].西安:西安交通大学,1995
[5]王丰饶.滑模变结构控制.北京:机械工业出版社,1995
[6]夏长亮,祁温雅,杨荣,等.基于RBF神经网络的超声波电机参数辨识与模型参考自适应控制.中国电机工程学报,2004,24(7):18-22
[7]田亮.模糊控制神经网络的无刷直流电机直接转矩控制系统研究:[硕士学位论文].天津:天津大学,2006
[8]王娟.基于RBF的无刷直流电机直接电流控制与转矩分析:[硕士学位论文].天津:天津大学,2003
[9]夏长亮,郭培健,史婷娜,等.基于模糊遗传算法的无刷直流电机自适应控制.中国电机工程学报,2005,25(11):129-133
[10]孙增圻.智能控制理论与技术.清华大学出版社,广西科学技术出版社,1997
[11]纪志成,沈艳霞,薛花.无刷直流电机自适应模糊控制的研究.中国电机工程学报,2005,25[5]:104-109
[12]刘启明.模糊滑模变结构控制设计.复旦学报:自然科学版,2004,43(5):952-955
[13]夏长亮,王娟,史婷娜,等.基于自适应径向基函数神经网络的无刷直流电机直接电流控制.中国电机工程学报,2003,23(6):123-12
[14]陈炜.无刷直流电机卡尔曼法转子位置检测及模型参考自适应控制:[硕士学位论文].天津;天津大学,2003
[15]张明君,张化光.遗传算法优化的BP神经网络PID控制器.电气自动化,2005,27(6):3-5
[16]李红利,张晓彤,兰立柱,等.基于遗传算法的RBF神经网络的优化设计方法.计算机仿真,2003,20(11):67-69
[17]石红瑞,刘勇,刘宝坤,等.基于混合递阶遗传算法的径向基神经网络学习算法及其应用.控制理论与应用,2002(8)
[18]郑丕谔,马艳华.RBF神经网络的递阶遗传训练新方法.控制与决策,2003(3)
[19]李方方,赵英凯,许必熙.基于递阶遗传算法优化神经网络的研究.机械与电子,2006(2)
[20]李正军.基于自抗扰控制器的无刷直流电机控制:[硕士学位论文].天津:天津大学,2004
[21]韩京清.非线性PID控制器[J].自动化学报,1994,20(4),487-490
[22]苏玉鑫,段宝岩.一种新型非线性PID控制器[J].控制与决策,2003,18(1),26-128
[23] K.Watanbe,M.Ito.A process-model control for linear systems with delay [J].IEEE Trans.Automat.Contr,1981,26(6),1261-1269
[24] M.R.Matausek,A.D.Micic.A modified Smith predictor for controlling a process with an integrator and long dead-time[J].IEEEE Transactions on Automatic Control,1994,39(2),343-345
[25] M R Matausek,A D Micic.A modified Smith Predictor for controlling a process with an integrator and long dead-time[J].IEEE Transactions on Automatic Control,1996,41(8),1199-1203
[26] M.R.Matausek,A D.Micic.On the modified Smith predictor for controlling a process with an integrator and long dead-time[J].IEEE Transactions on Automatic Control,1999,44(8),1603-1606
[27] S.Majhi,D P.Atherton.A new Smith predictor and controller for unstable and integrating processes with time delay[J].Proceeding of the IEEE conference on Control and decision,Tampa, USA,1998,1341-1345
[28] S Majhi,D P Atherton.Obtaining controller parameters for a new Smith predictor using autotuning[J].Automatica,2000,36,1651-1658
[29] S.Majhi,D P Atherton.Autotuning and controller design for processes with small time delays[J].IEE Proceedings of the Control Theory and Application,1999,146(5),415-425
[30] Ibrahim Kaya.Obtaining controller parameters for a new PI-PD Smith predictor using autotuning[J].Journal of Process Control,2003,13,465-472
[31]张卫东,孙优贤.一类Smith预估器及其鲁棒整定[J].自动化学报,1997,23(5),660~663
[32] TLiu Y Z.Cai,D Y Gu,W.D.Zhang.New modified Smith predictor scheme for integrating and unstable processes with time delay[J].IEE Proc-Control Theory Appl,2005,152(2):238-246
[33] Zhang W D,Sun Y X, Xu X M.Two degree-of-freedom Smith predictor for processes with time delay [J],Automation,1998,34(10):1279-1282
[34] Kaya I.IMC based automatic tuning method for PID controllers in a Smith predictor configuration[J].Computers and Chemical Eng,2004,28(3):281-290
[35] Nunez-Reyes A,Normey-Rico J E,Bordons C,et.A Smith predictive based MPC in a solar air conditioning plant[J].Journal of Process control,2005,15(1):1-10
[36] Tian Y C,Gao F R.Compensation of dominant and variable delay in process systems[J]. Ind Eng Chen Res,1998,37(3):982-986.
[37] Wang Q G,Guo X,Zhang Y.Direct identification of continuous time delaysystems from step responses[J].Journal of Process Control,2001,11(5):531-542.
[38]张国范,朱晓萍,包新华.一种分析和设计Smith预估器的新方法及其应用[J].控制与决策,2001,16(3):341-343.
[39] C.E.Garcia,M Morari.Internal model control[M].Ind Eng Chen Proc Des.Dev. 1982
[40]张井岗,刘志远.一类自横对象的二自由度PID控制[J].控制与决策,2002,17(6),886~889
[41]汤莉莉,刘开培.基于IMC的不稳定时滞过程PID控制[J] .武汉大学学报,2003,36(5),118-122
[42] Changliang Xia,Junhua Liu,Wei Yu, et al.Variable structure control of BLDCM based on extended state observer.Mechatronics and Automation,2005 IEEE International Conference Volume 2,2005:568-571
[43] Fengtai Huang,Dapeng Tien.A.Neural network approach to position sensorless control of brushless DC motors.Proc.IEEE IECON 22nd International Conference,1996,2:1167-1170
[44] Chang-Liang Xia,Wei Chen.Senseless control of brushless DC motors at low speed using neural networks . Machine Learning and Cybernetics ,2005.Proceedings of 2005 International Conference on Volume 2, 18-21 Aug.2005:1099-1103
[45]陈炜.无刷直流电机卡尔曼法转子位置检测及模型参考自适应控制:[硕士学位论文].天津:天津大学,2003
[46]夏长亮,祁温雅,杨荣,等.基于混合递阶遗传算法和RBF神经网络的超声波电动机自适应速度控制.电工技术学报,2004(9)
[47]夏长亮,祁温雅,杨荣,等.基于RBF神经网络的超声波电机参数辨识与模型参考自适应控制.中国电机工程学报,2004,24(7):18-22
[48] S Yilmaz, K Howard,T David A.Direct model reference adaptive control of permanent magnet brushless DC motors . IEEE Conference on Control Applications-Proceedings,1997.9:633-638
[49] K Kyeong-Hwa,Y Myung-Joong.DSP-based high-speed sensorless control for a brushless DC motor using a DC link voltage.Electric Power Components and Systems,2002,30(9):889-906
[50] S Tomonobu,T Mutuwo,D Shinji,et al.Sensorless vector control of brushless DC motors using disturbance observer.PESC Record-IEEE Annual Power Electronics Specialists Conference,1995,2:772-777
[51]孟繁荣,李一凡,龙三平.基于TMS320F2812的永磁同步电机调速系统设计.工业技术,2006(17):95-96
[52]贺益康.交流电机调速系统计算机仿真.浙江大学出版社,1993
[53] Changliang Xia,Junhua Liu,Wei Yu,et al.Variable structure control of BLDCM based on extended state observer.Mechatronics and Automation,2005 IEEEInternational Conference Volume 2,2005:568-571
[54]胡大可.MSP430系列FLASH型超低功耗16位单片机.北京:北京航空航天大学出版社,2001
[55]张唏,王德银,张晨.MSP430系列单片机实用C语言程序设计.北京:人民邮电出版社,2005
[56]宋鹏.基于DSP的盘式无铁心永磁同步电机直接转矩控制调速系统:[硕士学位论文].天津:天津大学,2006
[57]李志强.基于RBF神经网络在线辨识的永磁无刷直流电机单神经元PID自适应控制:[硕士学位论文].天津:天津大学,2006
[58] Matsui,Nobuyuki,Takeshita,et al.Novel starting method of sensorless salient-pole brushless motor.Conference Record– IAS Annual Meeting,1994,1:386-392
[59]刘文.微机控制的无位置传感器无刷直流电机及其调速系统:[硕士学位论文],西安:西安交通大学,1995