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Adaptive neural speed controller of a dc motor

Electric Power Systems Research47(1998)123–132

M.D.Minkova,D.Minkov*,J.L.Rodgerson,R.G.Harley

Department of Electrical Engineering,Uni6ersity of Natal,Durban4014,South Africa

Received23February1998;accepted30March1998

Abstract

An adaptive neural speed controller of a dc motor is proposed.The arti?cial neural network(ANN)is trained by the online backpropagation algorithm.The output of the ANN gives the control voltage applied to the dc motor.The difference between the reference and the actual rotor speed of the motor is backpropagated through the ANN at each step of the control process for updating the connection weights of the ANN.The control scheme requires neither a knowledge of any motor parameters,nor preferential training of the ANN.The performance of the controller is simulated depending on the rotor speed noise of the motor, the rapidity of its dynamics,the sampling period,and the sharp instantaneous change of the load,or in the reference speed trajectory.?1998Elsevier Science S.A.All rights reserved.

Keywords:DC motor;Adaptive speed controller;Arti?cial neural network

1.Introduction

The dynamics of a dc motor is described by the

following equations[1]:

K (t)=?Ri(t)?L d i(t)

d t

+w(t)(1)

Ki(t)=J d (t)

d t

+D (t)+T l(t)(2)

where: (t)is the rotor speed(rad s?1);R the armature resistance(V);L the armature inductance(H);w(t)the armature voltage(V);i(t)the armature current(A);T l the load torque(Nm);J the rotor inertia(kg m2);K is torque and back EMF constant(V s rad?1);and D is the viscous friction coef?cient(Nms rad?1).

In order to control a plant,a discrete time model of the plant is required.The following discrete time model of a dc motor is used in[1]:w(k)=A1 (k+1)+A2 (k)+A3 (k?1)

+A4(k,k?1)(3) where k indicates the k th discrete time moment,A1,A2, and A3are real constants,and A4is a real parameter which depends on the load of the motor.

There are two approaches for the application of ANNs for plant control.For certain uncomplicated plants,it is possible to train the ANN off-line,before its use in a non-adaptive controller.In this case,pre-control training of the ANN is carried out,but the connection weights of the ANN are not updated during the control.In the alternative approach,the connection weights of the ANN are updated on-line,during the control,and the controller is adaptive[2].The non-adaptive neural control of a dc motor has been investi-gated by El-Sharkawi et al.[1,3,4].An adaptive neural speed controller of a dc motor has been proposed by Buja et al.[5].It is assumed in[5]that the motor parameters K and J are known,but determination of these parameters is not indicated.

In this paper,an adaptive neural speed controller of a dc motor is proposed.The parameters of the con-troller are optimised,based on simulation of the perfor-mance of the controller depending on the rotor speed noise of the motor,the rapidity of its dynamics,the

*Corresponding author.Present address:Research Institute for Fracture Technology,Tohoku University,Aoba-Ku,Sendai980, Miyagi980,Japan.Tel.:+81222177519;fax:+81222252263; e-mail:dminkov@genesis.mech.tohoku.ac.jp

M .D .Minko 6a et al ./Electric Power Systems Research 47(1998)123–132

124Fig.1.Topology of the ANN used in the controller.

Fig.2.A schematic diagram of the adaptive neural speed controller of a dc motor.

sampling period,and the sharp instantaneous change of the load or in the reference speed trajectory.The idea for the design of the adaptive speed controller of a dc motor is based on the work of Henaff and Milgram [6]for adaptive robot control by feedforward ANN trained by the online backpropagation algorithm (OBP).It can be deduced from [6]that in cases where the desired output of the ANN is not known,the error at the output of the subsequent plant could be back-propagated through the ANN for updating its connec-tion weights.The update of the connection weights of the output nodes of the ANN is proportional to the negative of the gradient of the error at the plant output with respect to the output from the respective ANN output node rather than the negative of the unknown gradient of the error at the ANN output with respect to the output from the same node.The update of the connection weights of the hidden nodes obeys the same formula as in the online backpropagation algorithm [6].

2.The adaptive speed controller

2.1.The ANN topology

The ANN used in the controller is a feedforward ANN with one hidden layer,three inputs ?(k +1), (k ),and (k ?1),and one output w ?(k ).The ANN has three input nodes,nine hidden nodes,one output node,unit offset nodes in the input layer and the hidden layer,and activation of the hidden nodes and the output node by the sigmoidal activation function F .The output x (k )after the activation of the output node lies within the interval [0.4,0.6]with a length li =0.2,and is mapped onto the ANN output w ?(k )which lies within the interval [?w r ,w r ]by the following linear transformation:w ?(k )=

2w r li x (k )? 0.5?li

?w r

(4)

M .D .Minko 6a et al ./Electric Power Systems Research 47(1998)123–132125

Fig.3.A ?owchart of the operation of the adaptive neural speed controller of a dc motor.

where w r is the rated armature voltage.The inverse mapping of the ANN output w ?(k )onto the output x (k )after the activation of the output node is carried out by the transformation:x (k )=

0.5?

li 2

li[w ?(k )+w r ]2w r (5)

which is used during the ANN training.The ANN topology is shown in Fig.1.

2.2.The ANN training

The ANN is trained during the control process by

the online backpropagation algorithm.The initial val-ues of the connection weights of the ANN are ran-domly distributed in the interval [?0.1,0.1].The

update D w ij of the connection weights w ij of the ANN is:

D w ij (k +1)=kl i k F j k

+h D w ij (k )(6)where l i k is the error signal for the i th node at the k th discrete time moment,the learning rate is k =0.25,and h is the momentum parameter.

According to the concept of Henaff and Milgram [6],the ANN is trained during the control process by backpropagating the negative of the gradient of the

M .D .Minko 6a et al ./Electric Power Systems Research 47(1998)123–132

126Fig.4.Performance of the controller for the dc motor without a load for RST1and T =T r =50ms.k =0.25;h =0.7;li =0.2.

Fig.6.Performance of the controller for the dc motor without a load for RST3and T =T r =5ms.k =0.25;h =0.25;li =0.2.

error of the rotor speed at the output of the motor with respect to the control voltage:?(E (w ?(k )=?([1/2( rf ? )2]( (k )

( (w ?(k )

=[ rf (k )? (k )]

(

(k )

(7)

where rf (k )is the known reference speed in the k th

discrete time moment,instead of backpropagating the

negative of the unknown gradient w rf (k )?w

?(k )of the error at the ANN output with respect to the ANN output.

The direct application of Eq.(7)for updating the connection weights of the ANN is practically impossi-ble.The reason being that noise in the rotor speed of the motor can lead to values of ( /(w ?(k )which differ

greatly from the corresponding values without noise.In

fact,the relative error in ( /(w ?(k )due to the rotor speed noise of the motor can be much greater than the relative error in rf (k )? (k ).Generally,w rf (k )?w ?(k )8 rf (k )? (k ).Consequently,the control error m c (k )= rf (k )? (k )is backpropagated through the ANN for its training,instead of backpropagating the unknown quantity w rf (k )?w ?(k ).Correspondingly,the

error signal l o k

for the output node of the ANN is:

l o k =[ rf (k )? (k )]F o k (1?F o k

)

(8)

and the error signal l i k for a hidden node of the ANN is:

l i k =l o k w o i k F i k (1?F i k

)

(9)

according to the theory for the ANN training by the

Fig.7.Performance of the controller for the dc motor with a load for RST3and T =5ms.k =0.25;h =0.25;li =0.2.

Fig.5.Performance of the controller for the dc motor with a load for

RST1and T =50ms.k =0.25;h =0.7;li =0.2.

M .D .Minko 6a et al ./Electric Power Systems Research 47(1998)123–132127

Fig.8.Performance of the controller for the dc motor without a load for RST1and T =T r =50ms when white noise with an amplitude of 1rad s ?1is superimposed on the rotor speed of the dc motor.k =0.25;h =0.7;li =0.2.Fig.10.Performance of the controller for the dc motor without a load for RST2and T =T r =50ms when white noise with an ampli-tude of 1rad s ?1is superimposed on the rotor speed of the dc motor.k =0.25;h =0.7;li =0.2.

rf (k +1)=0.6 rf (k )+0.2 rf (k ?1)+r (k )

(10)

where r (k )is the input to the reference model,and the rotor speed of the dc motor has to follow the known reference speed trajectory rf (k )de?ned in the time interval [0,t f ].

The rotor speed is predicted in the (k +1)th moment as:

?(k +1)=0.6 (k )+0.2 (k ?1)+r (k ).

(11)

The ANN has three inputs: ?(k +1), (k ),and (k ?1),and one output w ?(k ).The armature voltage w ?(k )is estimated by the ANN as:w ?(k )=N [ ?(k +1), (k ), (k ?1)]

(12)

where N [ ?(k +1), (k ), (k ?1)]is the output of the ANN,in accordance with the discrete time model of the dc motor introduced by Eq.(3).The dc motor is controlled by the armature voltage w ?(k ),and the actual rotor speed (k )is sampled at the output of the motor.The input to the reference model r (k )and the actual rotor speed (k )at the output of the motor are sam-pled for the same sampling period T in the discrete time moment t =kT .A ?owchart of the operation of the controller is shown in Fig.3.

3.Simulated performance of the controller

Computations are carried out to simulate the speed control of a dc motor with the following parameters:R =1.2V ;L =25mH;J =0.208kg m 2;K =1.21V s rad ?1;D =0.008Nms rad ?1;T l =0for the dc motor without a load and T l =0.133 for the dc motor with a load;w r =240V and P r =5kW (where the subscript r indicates a rated value).

online backpropagation algorithm [7].The connection weights of the ANN are updated once at the start of every control step for only one training epoch.No training of the ANN is carried out before the start of the control process.

2.3.The control scheme

The proposed adaptive speed controller of a dc mo-tor is shown in Fig. 2.The following second order reference model is chosen:

Fig.9.Performance of the controller for the dc motor with a load for RST1and T =50ms when white noise with an amplitude of 1rad s ?1is superimposed on the rotor speed of the dc motor.k =0.25;h =0.7;li =0.2.

M.D.Minko6a et al./Electric Power Systems Research47(1998)123–132 128

Reference speed trajectories RST1,RST2,RST3,

and RST4are used in the simulations for adaptive

speed control of the dc motor.The sampling period

T during the control process is chosen to satisfy the

limitation (k)? (k?1) B1rad s?1,which is re-

quired for the replacement of the derivatives in Eqs.

(1)and(2)by?nite differences.The maximum sam-

pling period for which the limitation (k)? (k?

1) B1rad s?1is satis?ed for a given reference speed

trajectory is designated as T r.T r=50ms for RST1

and for RST2,and T r=5ms for RST3.The refer-

ence speed trajectory RST4is used with a maximum

sampling period T r=50ms due to the similarity be-

tween RST2and RST4,although the limitation

(k)? (k?1) B1rad s?1for the operating space

is not satis?ed in the region of the sharp instanta-

neous change of the reference speed for RST4.The

reference speed trajectories RST1,RST2,and RST4 with T r=50ms are considered to correspond to slow motor dynamics and the reference speed trajectory RST3with T r=5ms is assumed to correspond to fast motor dynamics.

The performance of the controller is shown in Fig. 4for the dc motor without a load,and in Fig.5for the dc motor with a load,both for the reference speed trajectory RST1and T=T r=50ms.Fig.6il-lustrates the performance of the controller for the dc motor without a load,and Fig.7for the dc motor with a load,both for RST3and T=T r=5ms.The comparison between Figs.4–7indicate that the per-formance of the controller is similar for the dc motor without a load and the dc motor with a load,inde-pendently from the reference speed trajectory,when Fig.12.Performance of the controller for the dc motor without a load for RST3and T=T r=5ms when white noise with an ampli-tude of1rad s?1is superimposed on the rotor speed of the dc motor. k=0.25;h=0.25;li=0.2.

the noise of the motor can be ignored.Figs.4–7 show that the adaptive speed controller performs in-adequately for faster motor dynamics.This is due to the imperfect on-line training of the ANN by only one training sample for a control step which results in imperfect prediction capability of the ANN espe-cially for fast motor dynamics where the reference speed changes rapidly and the ANN has not been trained suf?ciently for the latest characteristics of the change in the reference speed trajectory.

Fig.11.Performance of the controller for the dc motor with a load for RST2and T=50ms when white noise with an amplitude of1rad s?1is superimposed on the rotor speed of the dc motor.k=0.25; h=0.7;li=0.2.Fig.13.Performance of the controller for the dc motor with a load for RST3and T=5ms when white noise with an amplitude of1rad s?1is superimposed on the rotor speed of the dc motor.k=0.25; h=0.25;li=0.2.

M.D.Minko6a et al./Electric Power Systems Research47(1998)123–132129

Fig.14.Performance of the controller for the dc motor without a load for RST3and T=2ms when white noise with an amplitude of 1rad s?1is superimposed on the rotor speed of the dc motor. k=0.25;h=0.25;li=0.2.Fig.16.Performance of the controller for the dc motor without a load for RST3and T=8T r=40ms when white noise with an amplitude of1rad s?1is superimposed on the rotor speed of the dc motor.k=0.25;h=0.25;li=0.2.

4.In?uence of some factors on the performance of the controller

4.1.The rotor speed noise of the motor

The rotor speed noise of the motor is simulated by adding white noise with an amplitude of1rad s?1to the rotor speed at the output of the dc motor for slow motor dynamics introduced by the reference speed tra-jectories RST1and RST2with a maximum sampling period T=T r=50ms.The performance of the con-troller is shown in Fig.8for the dc motor without a load,and in Fig.9for the dc motor with a load,both for the reference speed trajectory RST1and T=T r= 50ms.Fig.10gives the performance of the controller for the dc motor without a load,and Fig.11for the dc motor with a load,both for RST2and T=T r=50ms. The comparison between Figs.4,5,10and11indicates that the white rotor speed noise of the motor with an amplitude of1rad s?1leads to a signi?cant rotor speed noise of the controller.Nevertheless,Figs.8–11show that the controller performs reasonably well for the dc motor both without a load and with a load for slow motor dynamics.

4.2.The rapidity of the motor dynamics

The control of a dc motor is simulated for fast motor dynamics introduced by the reference speed trajectory RST3,adding white noise with an amplitude of1rad s?1to the rotor speed at the output of the motor.The performance of the controller is shown in Fig.12for the dc motor without a load,and in Fig.13for the dc motor with a load.The comparison between Figs.6,7, 12and13shows that the1rad s?1white rotor speed noise of the motor results in an inadmissible increase of

Fig.15.Performance of the controller for the dc motor without a load for RST3and T=4T r=20ms when white noise with an amplitude of1rad s?1is superimposed on the rotor speed of the dc motor.k=0.25;h=0.25;li=0.2.Table1

Dependence of the parameters A1,A2,and A3as a function of the sampling period T of the motor

A1A2

T(ms)A3

8800.3

4504.2?4296.7

?171.9

5383.8

213.1

5.8?1.7

50 6.3

?0.1

0.0

200 1.1

M.D.Minko6a et al./Electric Power Systems Research47(1998)123–132 130

Fig.17.Performance of the controller for the dc motor without a load for RST1and T=4T r=200ms when white noise with an amplitude of1rad s?1is superimposed on the rotor speed of the dc motor.k=0.25;h=0.7;li=0.2.Fig.19.Performance of the controller for the dc motor without a load for RST2and T=4T r=200ms with1rad s?1white noise of the rotor speed when the load T l=0.133 is connected to the motor 50s after the beginning of the control.k=0.25;h=0.7;li=0.2.

the rotor speed noise of the controller for fast motor dynamics,for the dc motor both without a load,and with a load.

4.3.The sampling period

Control simulations are made for different values of the sampling period T used in the control of the dc motor without a load for the reference speed trajectory RST3when white noise with an amplitude of1rad s?1 is added to the rotor speed at the output of the motor. The performance of the controller for the dc motor without a load is shown in Fig.14for a sampling period T=2ms,in Fig.15for T=4T r=20ms,and in Fig.16for T=8T r=40ms.The comparison between Fig.12,Figs.14–16shows that an increment of the sampling period from T=T r to T=4T r results in a signi?cant decrease in the rotor speed noise of the controller,but a further increment of the sampling period to T=8T r makes the actual speed trajectory lag signi?cantly behind the reference speed trajectory.The controller performs reasonably well for the optimum sampling period of T=4T r for the dc motor without a load for the fast motor dynamics introduced by the reference speed trajectory RST3.

Fig.18.Performance of the controller for the dc motor without a load for RST2and T=4T r=200ms when white noise with an amplitude of1rad s?1is superimposed on the rotor speed of the dc motor.k=0.25;h=0.7;li=0.2.Fig.20.Performance of the controller for the dc motor without a load for RST2and T=4T r=200ms with1rad s?1white noise of the rotor speed when the load T l=0.133 is connected to the motor 110s after the beginning of the control.k=0.25;h=0.7;li=0.2.

M .D .Minko 6a et al ./Electric Power Systems Research 47(1998)123–132131

the initial control stages when the ANN training is insuf?cient.

4.4.The sharp instantaneous change of the load or in the reference speed trajectory

To estimate the adaptive capability of the controller,control simulations are made for the dc motor without a load for a sharp instantaneous connection of a load to the motor,and for a sharp change in the reference speed trajectory when 1rad s ?1white noise is added to the rotor speed at the output of the motor.The perfor-mance of the controller for the dc motor without a load is shown in Fig.19for the reference speed trajectory RST2when the load T l =0.133 is connected to the motor 50s after the beginning of the control,and in Fig.19when the load T l =0.133 is connected 110s after the beginning of the control,in both cases for a sampling period T =4T r =200ms.Figs.19and 20show that after a small peak in the actual speed trajec-tory due to the connection of the load,the actual speed trajectory converges to the reference speed trajectory independently when the load is connected to the motor.Fig.21shows the performance of the controller for the dc motor without a load for the reference speed trajectory RST4and a sampling period of T =T r =200ms.It is seen that the actual speed trajectory converges to the reference speed trajectory after a sharp change in the reference speed trajectory.Figs.19–21show that the actual speed trajectory converges to the reference speed trajectory about 5s after a sharp instantaneous change occurs either in the load of the motor,or in the reference speed trajectory.It indicates that :5/T =25control steps are necessary for the adaptation of the connection weights of the controller ANN to allow the controller to follow the reference speed trajectory for the dc motor without a load.

5.Conclusions

An adaptive controller of the rotor speed of a dc motor is proposed.The reference speed trajectory,the discrete time model of the motor,and the supply of training samples to the ANN are used for the same sampling period T .The controller uses an ANN which is trained during the control process by the online backpropagation algorithm.The error rf (k )? (k ),measured at the output of the motor is backpropagated through the ANN for one update of its connection weights at every control step.The parameters of the ANN training are the learning rate k ,the momentum parameter h ,and the length li of the interval in which the activated output x (k )from the output node of the ANN lies.No preferential training of the ANN is

Fig.21Performance of the controller for the dc motor without a load for RST4with 1rad s ?1white noise of the rotor speed.y =0.25;a =0.7;li =0.2.

The magnitude of the coef?cients A 1,A 2,and A 3which characterise the discrete time model of a dc motor introduced by Eq.(3)increases with the decrease of T ,according to Table 1.This results in an increment of the noise of the control voltage w ?(k )for a given noise of the rotor speed in the three successive moments ?(k +1), (k ),and (k ?1)according to Eq.(3).Considering that the ANN is trained by backpropaga-tion of the low noise error rf (k )? (k )through the ANN,i.e.the ANN is not trained to remove the large noise of the control voltage for a small sampling period T ,it results in a signi?cant increase in the rotor speed noise of the controller.For a sampling period T T r the ANN lacks training around the last control point on the reference speed trajectory,the ANN tends to predict time delayed values of the control voltage,and the actual speed trajectory lags behind the reference speed trajectory.

Control simulations are also made for the dc motor without a load for the reference speed trajectories RST1and RST2and a sampling period T =4T r =200ms adding 1rad s ?1white noise to the rotor speed at the motor output.Fig.17shows the performance of the controller for the reference speed trajectory RST1and T =4T r =200ms,and Fig.18for the reference speed trajectory RST2and T =4T r =200ms,both for the dc motor without a load.The comparison between Figs.8,10,12,15,17and 18shows that the increment of the sampling period from T =T r to T =4T r results in decrement of the noise of the controller and improve-ment of its performance for the dc motor without a load,independently from the rapidity of the motor dynamics.Figs.15,17and 18indicate that a signi?cant noise in the actual rotor speed trajectory occurs only in

M.D.Minko6a et al./Electric Power Systems Research47(1998)123–132 132

carried out before the beginning of the control.The controller does not require knowledge of any motor/ load parameters.

The speed control is reasonably good for the dc motor both without a load and with a load for T=T r, when the rotor speed noise of the motor can be ne-glected.The controller performs inadequately for fast motor dynamics.The presence of1rad s?1white noise in the rotor speed of the motor leads to a signi?cant rotor speed noise of the controller for T=T r,and the noise of the controller could become unacceptable for fast motor dynamics.An increment of the sampling period from T=T r to T=4T r leads to a decrement of the noise of the controller and an improvement of its performance.Further increment of the sampling period to T=8T r results in lagging of the actual speed trajec-tory behind the reference speed trajectory.The sam-pling period which gives the best performance of the adaptive speed controller is T=4T r,independently of the reference speed trajectory.The controller ANN adapts to sharp instantaneous changes of the load connected to the motor,or in the reference speed trajectory.The actual speed trajectory converges to the reference speed trajectory for T=4T r within an interval of:25sampling periods after a sharp instantaneous change.

References

[1]S.Weerasooriya,M.A.El-Sharkawi,Identi?cation and control

of a dc motor using back-propagation neural networks,IEEE Trans.Energy Convers.6(1991)663–669.

[2]P.J.Antsaklis,D.P.Atherton,K.Warwick,Neural Networks for

Control and Systems,Short Run Press,UK,1992,pp.31–68.

[3]S.Weerasooriya,M.A.El-Sharkawi,Laboratory implementation

of a neural network trajectory controller for a dc motor,IEEE Trans.Energy Convers.8(1993)107–113.

[4]M.A.El-Sharkawi,A.A.El-Samahy,High performance drive of

dc brushless motors using neural network,IEEE Trans.Energy Convers.9(1994)317–322.

[5]M.Bertoluzzo,G.S.Buja,F.Todesco,Neural network adaptive

control of a dc drive,Proc.Conf.IEEE Ind.Electronics Soc., Italy,1994,pp.1232–1236.

[6]P.Henaff,https://www.wendangku.net/doc/ba10732206.html,gram,Adaptive neural control with backprop-

agation algorithm,Proc.IMACS Int.Symp.Signal Process., Robotics and Neural Networks,France,1994,pp.395–398. [7]B.J.A.Krose,P.P.van der Smagt,An Introduction to Neural

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30–37.

(各电机设计软件对比)电磁场软件对比优势

Infolytica软件与同类软件的区别 Infolytica与Ansys、Ansoft、Flux软件对比如下：

●这里主要介绍下Infolytica与Ansoft、Flux对比中的优势： ?建模方面：Infolytica应用于任何二维、三维结构建模，可导入、导出其他格式，如SA T、Pro/E、Catia、STEP、IGES、Investor等，模型识别能力较强。 Ansoft Maxwell、Flux模型识别能力方面不好，导出的cad模型dxf图纸不能直接标注。 ?剖分功能：Infolytica具有网格自适应剖分功能和求解阶次自适应功能，具备市场唯一的二维1~4阶和三维1~3阶求解能力，可以在保证精度的情况下，快速求解2D/3D问题。而Ansoft网格剖分技术只适合于低端或二维领域，也只有在二维领域才能跟Infolytica相提并论，在处理三维大型复杂问题时则明显不足。 ?3D电磁分析中：速度和精度上Infolytica软件高于Ansoft和Flux软件。 ?二次开发方面：Infolytica具有丰富的脚本和操作过程详细而简洁的函数记录，非常方便使用者二次开发。而Ansoft、Flux 操作记录非常复杂, 给二次开发带来困难。Ansoft通过宏来实现，对用户的编程能力要求太高。 ?不同之处：Infolytica具有市场上唯一支持六自由度和多运动部件瞬态运动求解器，而Ansoft、Flux不具备这两种功能。 ?多参数和多目标优化：Infolytica强大的参数化功能，结合优化模块OptiNet 可以进行多参数和多目标的优化，Flux这个功能较好，Ansoft有这个功能，但没有温度功能，更不能对磁热耦合结果进行优化。 ?全球5大领先优势：磁场MagNet和电场ElecNet的耦合，应用粒子加速、 CRT电子轨迹和电弧研究；磁场MagNet和温度场ThermNet双向耦合分析；电场ElecNet和温度场ThermNet双向耦合分析；优化模块OptiNet可以优化磁场MagNet 和温度场ThermNet耦合结果、电场ElecNet和温度场ThermNet 耦合结果；电磁场的六自由度、多运动体的独家分析能力。

尊重的素材

尊重的素材（为人处世）思路人与人之间只有互相尊重才能友好相处要让别人尊重自己，首先自己得尊重自己尊重能减少人与人之间的摩擦尊重需要理解和宽容尊重也应坚持原则尊重能促进社会成员之间的沟通尊重别人的劳动成果尊重能巩固友谊尊重会使合作更愉快和谐的社会需要彼此间的尊重名言施与人，但不要使对方有受施的感觉。帮助人，但给予对方最高的尊重。这是助人的艺术，也是仁爱的情操。—刘墉卑己而尊人是不好的，尊己而卑人也是不好的。———徐特立知道他自己尊严的人，他就完全不能尊重别人的尊严。———席勒真正伟大的人是不压制人也不受人压制的。———纪伯伦草木是靠着上天的雨露滋长的，但是它们也敢仰望穹苍。———莎士比亚尊重别人，才能让人尊敬。———笛卡尔谁自尊，谁就会得到尊重。———巴尔扎克人应尊敬他自己，并应自视能配得上最高尚的东西。———黑格尔对人不尊敬，首先就是对自己的不尊敬。———惠特曼

每当人们不尊重我们时，我们总被深深激怒。然而在内心深处，没有一个人十分尊重自己。———马克·吐温忍辱偷生的人，绝不会受人尊重。———高乃依敬人者，人恒敬之。———《孟子》人必自敬，然后人敬之；人必自侮，然后人侮之。———扬雄不知自爱反是自害。———郑善夫仁者必敬人。———《荀子》君子贵人而贱己，先人而后己。———《礼记》尊严是人类灵魂中不可糟蹋的东西。———古斯曼对一个人的尊重要达到他所希望的程度，那是困难的。———沃夫格纳经典素材 1元和200元（尊重劳动成果）香港大富豪李嘉诚在下车时不慎将一元钱掉入车下，随即屈身去拾，旁边一服务生看到了，上前帮他拾起了一元钱。李嘉诚收起一元钱后，给了服务生200元酬金。这里面其实包含了钱以外的价值观念。李嘉诚虽然巨富，但生活俭朴，从不挥霍浪费。他深知亿万资产，都是一元一元挣来的。钱币在他眼中已抽象为一种劳动，而劳动已成为他最重要的生存方式，他的所有财富，都是靠每天20小时以上的劳动堆积起来的。200元酬金，实际上是对劳动的尊重和报答，是不能用金钱衡量的。富兰克林借书解怨（尊重别人赢得朋友）

感官动词和使役动词

感官动词和使役动词默认分类2010-05-28 23:14:26 阅读46 评论0 字号：大中小订阅使役动词，比如let make have就是3个比较重要的 have sb to do 没有这个用法的只有have sb doing.听凭某人做某事 have sb do 让某人做某事 have sth done 让某事被完成（就是让别人做）另外：使役动词 1.使役动词是表示使、令、让、帮、叫等意义的不完全及物动词,主要有make(使,令), let(让), help(帮助), have(叫)等。 2.使役动词后接受词,再接原形不定词作受词补语。 He made me laugh. 他使我发笑。 I let him go. 我让他走开。 I helped him repair the car. 我帮他修理汽车。 Please have him come here. 请叫他到这里来。 3.使役动词还可以接过去分词作受词补语。 I have my hair cut every month. 我每个月理发。 4.使役动词的被动语态的受词补语用不定词,不用原形不定词。 (主)He made me laugh. 他使我笑了。 (被)I was made to laugh by him. 我被他逗笑了。使役动词有以下用法： a. have somebody do sth让某人去做某事 ??i had him arrange for a car. b. have somebody doing sth.让某人持续做某事。 ??he had us laughing all through lunch. 注意：用于否定名时，表示“允许” i won't have you running around in the house. 我不允许你在家里到处乱跑。 ******** 小议“使役动词”的用法 1. have sb do 让某人干某事 e.g:What would you have me do? have sb/sth doing 让某人或某事处于某种状态，听任 e.g: I won't have women working in our company. The two cheats had the light burning all night long. have sth done 让别人干某事，遭受到 e.g:you 'd better have your teeth pulled out. He had his pocket picked. notes: "done"这个动作不是主语发出来的。 2.make sb do sth 让某人干某事 e.g:They made me repeat the story. What makes the grass grow?

无刷直流电机软件的设计

4.3 控制器软件设计软件设计是控制系统最重要的一个组成部分，软件设计的好坏直接关系着整个控制系统性能的优良，控制系统的软件设计一定要具备实时性、可靠性和易维护性，对此，选择一款简单、方便的开发环境对于系统软件的整体优化以及提高整个系统的开发效率有很大的影响。目前支持STM 32系列控制芯片且应用比较广泛的主要有IAR EWARM和KEIL MDK这两个集成开发环境，本文采用的开发环境是KEIL MDK，它是ARM 公司推出的嵌入式微控制器开发软件，集成了业界领先的Vision 4开发平台，具有良好的性能，是ARM开发工具中的最好的选择，适合于不同层次的开发人员使用，尤其是它与我们经常使用的51单片机开发环境Keil C51的整体布局和使用方法类似，只有一些地方不同，操作起来比较熟练，很容易上手，极大的减小了开发人员的使用难度，缩短了开发周期，提高了开发效率，因此这款KEIL MDK得到了很多人的认可。 STM 32的软件开发主要开发方式有2种，就是基于寄存器的开发和基于库函数的开发，其中基于寄存器的开发方式就更51单片机的开发差不多，它是通过直接操作芯片内部的各个寄存器来达到控制芯片的目地，这种方式较直观，程序运行占用的资源少，但对于STM 32这种寄存器数目非常多的芯片来说，采用寄存器的开发方式会减慢开发速度，还让程序可读性降低。而基于库函数的开发方式则是对寄存器的封装，它向下处理与寄存器直接相关的配置，向上为用户提供配置寄存器的接口，这种方式大大降低了使用STM 32的条件，不仅提高了开发效率，而且程序还具有很好的可读性和移植性，因此本文采用的是基于库函数的开发方式，编程语言全采用 C 语言。

感官动词的用法

感官动词 1.see, hear, listen to, watch, notice等词，后接宾语，再接省略to的动词不定式或ing形式。前者表全过程，后者表正在进行。句中有频率词时，以上的词也常跟动词原形。注释:省略to的动词不定式--to do是动词不定式，省略了to,剩下do，其形式和动词原形是一样的，但说法不同。 see sb do sth 看到某人做了某事 see sb doing sth 看到某人在做某事 hear sb do sth 听到某人做了某事 hear sb doing sth 听到某人在做某事以此类推... I heard someone knocking at the door when I fell asleep. (我入睡时有人正敲门,强调当时正在敲门) I heard someone knock at the door three times. (听到有人敲门的全过程) I often watch my classmates play volleyball after school. (此处有频率词often) （了解）若以上词用于被动语态，须将省略的to还原： see sb do sth----sb be seen to do sth hear sb do sth----sb be seen to do sth 以此类推... We saw him go into the restaurant. → He was seen to go into the restaurant. I hear the boy cry every day. → The boy is heard to cry every day. 2.感官动词look, sound, smell, taste, feel可当系动词，后接形容词。 He looks angry. His explanation sounds reasonable. The cakes smell nice.

英语中感官动词的用法

英语中感官动词的用法一、感官动词 1、感官动词(及物动词)有：see/notice/look at/watch/observe/listen to/hear/feel(Vt)/taste(Vt)/smell(Vt) 2、连缀动词(含感官不及物动词) be/get/become/feel/look/sound/smell/taste/keep/stay/seem/ appear/grow/turn/prove/remain/go/run 二、具体用法： 1、see, hear, smell, taste, feel,这五个动词均可作连系动词，后面接形容词作表语，说明主语所处的状态。其意思分别为"看/听/闻/尝/摸起来……"。除look之外，其它几个动词的主语往往是物，而不是人。例如：These flowers smell very sweet.这些花闻起来很香。 The tomatoes feel very soft.这些西红柿摸起来很软。 2、这些动词后面也可接介词like短语，like后面常用名词。例如：Her idea sounds like fun.她的主意听起来很有趣。 3、这五个感官动词也可作实义动词,除look(当"看起来……"讲时)只能作不及物动词外,其余四个既可作及物动词也可作不及物动词,此时作为实义动词讲时其主语一般为人。例如:She smelt the meat.她闻了闻那块肉。 I felt in my pocket for cigarettes.我用手在口袋里摸香烟。 4、taste, smell作不及物动词时,可用于"t aste / smell + of +名词"结构,意为"有……味道/气味"。例如:The air in the room smells of earth.房间里的空气有股泥土味。 5、它们(sound除外)可以直接作名词,与have或take构成短语。例如：May I have a taste of the mooncakes?我可以尝一口这月饼吗？taste有品位、味道的意思。例如：I don’t like the taste of the garlic.我不喜欢大蒜的味道。 She dresses in poor taste.她穿着没有品位。 look有外观，特色的意思，例：The place has a European look.此地具有欧洲特色。 feel有感觉，感受的意思，watch有手表，观察的意思。例：My watch is expensive.我的手表很贵。 6、其中look, sound, feel还能构成"look / sound / feel + as if +从句"结构，意为"看起来/听起来/感觉好像……"。例如：It looks as if our class is going to win.看来我们班好像要获胜了。 7、感官动词+do与+doing的区别： see, watch, observe, notice, look at, hear, listen to, smell, taste, feel + do表示动作的完整性，真实性；+doing 表示动作的连续性，进行性。 I saw him work in the garden yesterday.昨天我看见他在花园里干活了。(强调"我看见了"

感官动词的用法

1.感官动词用法之一：see, hear, listen to, watch, notice等词，后接宾语，再接动词原形或ing形式。前者表全过程，后者表正在进行。句中有频率词时，以上的词也常跟动词原形。 I heard someone knocking at the door when I fell asleep. (我入睡时有人正敲门) I heard someone knock at the door three times. (听的是全过程) I often watch my classmates play volleyball after school.(此处有频率词often) 若以上词用于被动语态，后面原有动词原形改为带to不定式： We saw him go into the restaurant. →He was seen to go into the restaurant. I hear the boy cry every day. →The boy is heard to cry every day. 2.感官动词用法之二：look, sound, smell, taste, feel可当系动词，后接形容词： He looks angry. It sounds good. The flowers smell beautiful. The sweets taste sweet. The silk feels soft. I felt tired. They all looked tired. 这些动词都不用于被动语态。如：The sweets are tasted sweet.是个病句。注意：如果加介词like,则后不可接形容词，而接名词或代词：

尊重议论文

谈如何尊重人尊重他人，我们赢得友谊；尊重他人，我们收获真诚；尊重他人，我们自己也获得尊重；相互尊重，我们的社会才会更加和谐. ——题记尊重是对他人的肯定，是对对方的友好与宽容。它是友谊的润滑剂，它是和谐的调节器，它是我们须臾不可脱离的清新空气。“主席敬酒，岂敢岂敢？”“尊老敬贤，应该应该！”共和国领袖对自己老师虚怀若谷，这是尊重；面对许光平女士，共和国总理大方的叫了一声“婶婶”，这种和蔼可亲也是尊重。尊重不仅会让人心情愉悦呼吸平顺，还可以改变陌生或尖锐的关系，廉颇和蔺相如便是如此。将相和故事千古流芳:廉颇对蔺相如不满，处处使难，但蔺相如心怀大局，对廉颇相当的尊重，最后也赢得了廉颇的真诚心，两人结为好友，共辅赵王，令强秦拿赵国一点办法也没有。蔺相如与廉颇的互相尊重，令得将相和的故事千百年令无数后人膜拜。现在，给大家举几个例子。在美国，一个颇有名望的富商在散步时，遇到一个瘦弱的摆地摊卖旧书的年轻人，他缩着身子在寒风中啃着发霉的面包。富商怜悯地将8美元塞到年轻人手中，头也不回地走了。没走多远，富商忽又返回，从地摊上捡了两本旧书，并说：“对不起，我忘了取书。其实，您和我一样也是商人！”两年后，富商应邀参加一个慈善募捐会时，一位年轻书商紧握着他的手，感激地说：“我一直以为我这一生只有摆摊乞讨的命运，直到你亲口对我说，我和你一样都是商人，这才使我树立了自尊和自信，从而创造了今天的业绩??”不难想像，没有那一句尊重鼓励的话，这位富商当初即使给年轻人再多钱，年轻人也断不会出现人生的巨变，这就是尊重的力量啊可见尊重的量是多吗大。大家是不是觉得一个故事不精彩，不够明确尊重的力量，那再来看下一个故事吧！一家国际知名的大企业，在中国进行招聘，招聘的职位是该公司在中国的首席代表。经过了异常激烈的竞争后，有五名年轻人，从几千名应聘者中脱颖而出。最后的胜出者，将是这五个人中的一位。最后的考试是一场面试，考官们都作文话题素材之为人处世篇：尊重思路人与人之间只有互相尊重才能友好相处要让别人尊重自己，首先自己得尊重自己尊重能减少人与人之间的摩擦尊重需要理解和宽容尊重也应坚持原则尊重能促进社会成员之间的沟通尊重别人的劳动成果尊重能巩固友谊尊重会使合作更愉快和谐的社会需要彼此间的尊重名言施与人，但不要使对方有受施的感觉。帮助人，但给予对方最高的尊重。这是助人的艺术，也是仁爱的情操。———刘墉卑己而尊人是不好的，尊己而卑人也是不好的。———徐特立知道他自己尊严的人，他就完全不能尊重别人的尊严。———席勒真正伟大的人是不压制人也不受人压制的。———纪伯伦草木是靠着上天的雨露滋长的，但是它们也敢仰望穹苍。———莎士比亚

-单相电机设计软件说明

单相电机设计软件说明1程序结构 SOPT.EXE——运算程序，SIN.DAT——输入数据 SOUT.DAT——输出数据，DARW.DAT——特性曲线输出数据2开关功能设定 3槽形数据

5调速抽头形式 L型副相抽头T型主相抽头 KK2≠0，KK3=0 KK2=0，KK3≠0 6转子端环 7部分输入数据介绍 Xroll rat2——转子端环电阻修整值 dbfw——风磨耗，轴承磨耗，铁损等修整值 8电机负序电流为0 的条件： Im*Nm*KDPM=Ia*Na*KDPA θ=90° 9电机的运转电容与其交流电阻的对应关系：（仅供参考） 10正玄绕组分布要求（千分之总根数）

续前表

11 单相电机设计软件参数说明（标准泵2.2KW为例） --------------------------------------------------- Optimum (T) or calculation (F) 优化（T）或计算（F）一般为计算 ---------------------------------------------------- OPT ? (优化？) F （不是） ---------------------------------------------------- switch varibles 开关变量 ---------------------------------------------------- K1 K2 K3 K4 K5 K6 K7 （见说明） 2 1 3 3 2 3 2 --------------------------------------------------- Performance for design 设计要求（功率，电压，极数，相数，频率，效率，功率因素，起动电流） ---------------------------------------------------- Pn V P M F Efn Cosfi Ist1 2200.0 220.0 2.0 2 50.0 .78 .96 50.0 ---------------------------------------------------- Data of stator（定子外圆，对边，单边气隙，定转子内径，叠高，定转子槽数）长度单位cm ---------------------------------------------------- D1 F1 D Di1 Di2 L1 Q1 Q2 13.600 13.600 .030 7.000 3.000 12.00 24.00 18.0 ---------------------------------------------------- Data of rotor-ring and Capacitor（端环尺寸，叠压系数，转子通风孔直径，电容及其交流电阻） ---------------------------------------------------- Dor Dir A B1 B2 Kfe Dsk C Rc （见说明） 6.800 4.400 .500 1.200 1.100 .980 .000 45.0 4.0 ---------------------------------------------------- Dimension of stator slot（定子大槽尺寸，见说明） ---------------------------------------------------- Bs0 Bs1 Bs2 Hs0 Hs1 Hs2 Hs 0.25 .540 .880 .070 .070 1.300 1.440 ---------------------------------------------------- Dimension on small slot (As shape of slot not equal) （定子小槽尺寸，见说明） ---------------------------------------------------- Bs0s Bs1s Bs2s Hs0s Hs1s Hs2s Hss 0.000 0.00 .000 .000 .000 .000 .000

感官动词

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电机设计speed

强大的设计能力 SPEED是一款以磁路计算为主，有限元为辅的电机设计软件。常规的磁路法设计电机，主要是使用公式结合经验来做的，无法考虑控制电路也不能计算电机内的温度场，设计的精确性受经验的影响比较大。而SPEED软件是由很多有经验的工程师编写的，并经过许多著名的电机厂检验，同时还可以通过有限元对其进行验证。对于控制类电机，在其内部可以考虑很多种的控制方式。SPEED中还包含温度场分析。快速强大的求解功能 Speed的计算速度非常快。初步设计只需要10分钟，优化设计在1小时左右。因此，我们可以在很短的时间内设计出多种优化设计方案，并找到最符合要求的电机。开放的接口程序 Speed软件与其它软件有接口程序，如Autocad，Excel,Simulink和VB等，可以和许多商业化的有限元软件耦合进行仿真，同时可以使用子程序进行二次开发。

功能 SRD-开关磁阻电机模块有效的使用者操作界面操作手册包含广泛的技术性附录图形轮廓的编辑器、样板编辑器设计表单包含超过100种的输出数据由几何形状自动计算磁化曲线电流和转矩波形的计算，能量换算回路计算线圈绕组、控制晶闸管、二极管内的电流、转矩、效率和各项损耗外部磁化曲线的输出/输入功能有限元软件的连接控制选项的范围，例如电流调整一般/短磁束路径：单相范围设定（批次设计）硅钢片的材料数据库用以显示标准设计的热键功能

PC-BDC模块---无刷永磁马达以及控制有效的使用者操作界面操作手册包含广泛的技术性附录图形轮廓的编辑器、样板编辑器设计表单包含超过200种输出数据多种控制器模型选择，包括旋波、方波和交流电压等快速磁路计算可进行损耗计算开路时由于齿槽效应的磁铁损耗无感应操作的反电动势值侦测波形 9种标准转子类型线圈绕组编辑器：4种标准绕组类型，加上非整数槽的完全客制化的自动绕组空气间隙线圈绕组的近似计算波形的谐波分析有限元的连接硅钢片与磁铁的材料数据库电流谐波的引进换向开关的损耗源自交流线用于分相马达电容的平衡操作自感、互感和同步电感包含电流、电动势和转矩波形的模拟用于显示标准设计的热键功能