常州大学学报稿费

常州大学学报稿费

不是中文核心，是科技核心

常州大学学报（自然科学版）于1989年创刊，由江苏省教育厅主管，常州大学主办。
　　《常州大学学报（自然科学版）》是由江苏省教育厅主管、常州大学主办的自然科学与工业技术类学术刊物，是江苏省一级期刊。该刊被中国知网、万方、维普等数据库收录，是《中国学术期刊综合评价数据库》《万方数据库》《中文科技期刊数据库（全文版）》以及美国《化学文摘》、美国《剑桥科学文摘》和波兰《哥白尼索引》等数据库的来源期刊。开设的主要栏目有：化学化工、材料科学与工程、石油与天然气工程、环境科学与工程、机械制造及其自动化、计算机与信息工程、热能工程、生物医学工程、安全工程及数理科学等。

韩燕的发表论文情况

字母词的现状及未来—基于第5版和第6版《现代汉语词典》的比较研究，《苏州教育学院学报》，2014年第2期高校学生对汉语中英语外来词的态度及使用习惯调查，《常州信息职业技术学院学报》，2014年第1期汉语中英源外来词的本土化顺应研究，《中华文化论坛》，2013年第12期从语言接触看语言文化迁移—以汉语中的英语借词为对象，《常州大学学报》，2013年第5期适度文化移情对文化生态的平衡效用，《西南农业大学学报》，2012年第10期从跨文化能力角度解读常州文化创意产业发展的途径与方向，《常州工学院学报》，2012年第5期定势对文化移情能力的消解，《重庆交通大学学报》，2013年第2期英语习语中文化定势现象的认知解读，《苏州教育学院学报》，2011年第3期框架理论视角下定势的认知建构，《福建教育学院学报》，2011年第2期

常州大学代码是多少？

常州大学代码是10292，院校代号是全国各高校录取时为方便考生填报志愿而加注的由数字组成的代号串，即院校代码或学校代码。院校代码就如同是学校的一个身份证号，方便查询学校信息

常州大学简称“常大”，位于江苏省常州市，是江苏省人民政府与中国石油天然气集团有限公司、中国石油化工集团有限公司及中国海洋石油集团有限公司共建的省属全日制本科院校，为全国深化创新创业教育改革示范高校，国家知识产权试点示范高校，国务院侨办华文教育基地，江苏高水平大学建设B类高校  。

学校始建于1978年，前身为南京化工学院无锡分院、常州分院。1981年经国务院批准，学校定名为江苏化工学院；1992年成为中国石油化工集团公司管理的部属院校，并更名为江苏石油化工学院；2002年更名为江苏工业学院；2010年更名为常州大学；2020年学校成为江苏省教育厅与常州市人民政府共建高校。

学校被授予“全国模范职工之家”、“全国五四红旗团委”、“江苏省文明单位”、“江苏省教育人才工作先进单位”、“江苏省科技工作先进高校”、“江苏省高校思想政治教育工作先进集体”、“江苏省高等学校信息化建设优秀单位”、“江苏省高校图书馆先进集体”、“江苏省平安校园”、“江苏省花园式校园”等荣誉称号。

学校历史

1978年3月19日，江苏省化工局电话通知无锡市化工局：江苏省委决定成立南京化工学院无锡分院，要求先依托无锡市化工局负责组建和筹办；3月20日，无锡化工局党委派人赴南京接受任务；3月21日，由党委书记施乐尧主持的局党委会达成了筹建共识；4月17日，江苏省革委会发文成立南京化工学院无锡分院。

1978年3月20日，常州市化工局局长马申扬、副局长蒋光前向时任常州市委书记何冰皓、副书记章化农汇报，请市委同意并争取在常州创办化工分院；3月22日，经常州市革委会研究同意后，市化工局副局长蒋光前、技术科科长周祥生带着在常州化工厂内办学的初步方案，赴省化工局向胡萃华局长和顾静副局长汇报，请省化工局同意。经省化工局专题研究，与原省教育局衔接后，确定在常州创办化工分院的方案，并报江苏省委。时任江苏省委第一书记许家屯对创办常州分院作了批示；4月28日，省革委会发文成立南京化工学院常州分院。

1979年5月26日，江苏省革委会批复省化工局、无锡、常州市革委会建立南京化工学院无锡分院，并将常州分院并入无锡分院，校址设在无锡马山。合并后的南京化工学院无锡分院，实行省、市双重领导，以省管为主的领导体制，学制四年，在校生规模1200人，毕业生由江苏省统一分配。

1980年12月1日，江苏省化学石油工业厅、高等教育厅联合向江苏省人民政府呈交报告：经与常州市领导协商，并征得无锡市领导、省计委同意，拟将南京化工学院无锡分院迁至常州市办学；12月29日，江苏省人民政府批复：同意将南京化工学院无锡分院迁址常州办学，并改名为“江苏化工学院”。

1992年5月，江苏省人民政府与中国石油化工总公司向国家教委递交《关于申请改变江苏化工学院领导体制的函》；8月4日，国家教委复函，同意江苏化工学院改变现行领导体制，实行以中国石油化工总公司和江苏省人民政府共同领导，以中国石油化工总公司为主的领导管理体制，并改名为江苏石油化工学院，近期规模为3000人。

2000年，实行中央与地方共建、以江苏省管理为主的管理体制。

2001年，江苏省商业技工学校并入江苏石油化工学院。

2022年4月，首批入选教育部-瑞士GF智能制造创新实践基地培育建设单位。

师资力量

截至2021年4月，学校有教职工2000余人，专任教师中高级专业技术职务670余人，博士学位教师占比超过60%。拥有工程院院士5人（双聘），教育部“长江学者”特聘教授1人，国家“万人计划”教学名师2人，国家杰出青年科学基金获得者1人，“新世纪百千万人才工程”国家级人选4人，教育部新世纪优秀人才支持计划4人，享受国务院政府特殊津贴专家13人；有江苏省“双创人才”、“333工程”、“特聘教授”、“青蓝工程”等省级人才180余人；省“双创团队”、“青蓝工程”科技创新和教学团队、“六大人才高峰”创新人才团队等团队10余个。

学科建设

截至2021年4月，学校设有21个学院，另设有继续教育学院（李公朴社会教育学院）和常州大学怀德学院（独立学院），有本科专业80个；省优势学科2个、省“十三五”重点学科7个，国家级特色专业3个、国家级一流专业建设点12个、教育部专业综合改革试点项目2个、中国工程教育认证专业8个；国家级一流课程13门。

国家特色专业：化学工程与工艺、高分子材料与工程、过程装备与控制工程

国家级一流本科专业：化学工程与工艺、安全工程、过程装备与控制工程、高分子材料与工程、计算机科学与技术、制药工程、环境工程、能源化学工程、软件工程、会计学

学生成绩

“十三五”期间，学校获“挑战杯”全国大学生课外学术科技作品竞赛特等奖2项、一等奖3项、二等奖5项、三等奖6项，2017年捧得大挑“优胜杯”；获“挑战杯”全国大学生创业计划竞赛金奖“四连冠”，2020年捧得小挑“优胜杯”；获“互联网＋”大学生创新创业大赛金奖1项、银奖3项、铜奖5项。

学术科研

截至2021年4月，学校拥有国家地方联合工程研究中心1个、国家级重点实验室（培育点）1个、省部级重点实验室12个、省级协同创新中心2个、省级人文社科研究基地7个、省级大学科技园孵化器1个；与企业共建省级工程技术中心22个、校企联合研发中心43个、产学研基地487个、校企联盟432个。

截至2021年4月，“十三五”以来，学校获国家科技进步二等奖1项、国家技术发明二等奖1项、中国专利银奖1项、全国社科优秀成果奖二等奖1项、省部级和行业科研成果奖项97项；获得国家级科技项目274项，其中重大、重点项目17项；被SCI、EI、CPCI、CSSCI等收录学术论文3400余篇，获发明专利授权1700余件。

学术资源

截至2021年4月，学校图书馆有武进科教城校区、西太湖校区二个校区图书馆，馆舍面积40000多平方米，中外文纸质图书176多万册，阅览座位2800余个，电子数据库60多个，中外文电子图书168多万册；是高等教育文献保障系统（CALIS）和江苏省高等教育文献保障系统（JALIS）成员馆，是江苏省高校图书馆常州地区中心馆。

学术期刊

《常州大学学报（自然科学版）》是由江苏省教育厅主管、常州大学主办的自然科学与工业技术类学术刊物，是江苏省一级期刊和RCCSE中国核心期刊；于2020年首次被列为俄罗斯《文摘杂志》（AJ）来源期刊，同时也是美国《化学文摘》《剑桥科学文摘》《乌利希期刊指南》和波兰《哥白尼索引》等数据库的来源期刊，2017年与美国《艾博思科数据库（网络版）》签订了合作出版协议，并且已在海外发行；同时，是中国知网、万方、维普及超星等数据库及在线出版平台的来源期刊；开设的主要栏目有：《材料科学与工程》《化学化工》《环境科学与工程》《机械制造及其自动化》《石油与天然气工程》《计算机与信息工程》《生物医学工程》等；2016年12月，获“编辑出版质量优秀科技期刊”；2016年12月，“材料科学与工程”栏目获江苏科技期刊“金马奖”十佳品牌栏目；2017年12月，获精品科技期刊项目；2018年10月，获2018年度中国高校优秀科技期刊；2019年6月，获国家新闻出版署出版融合发展（武汉）重点实验室第一批“学术期刊融合出版能力提升计划项目”A类项目资助。

《常州大学学报（社会科学版）》主要设有《政治•法学研究》《经济•管理学研究》《文史哲研究》《艺术学研究》《社会学研究》等栏目；该刊为全国高校优秀社科期刊、RCCSE中国核心学术期刊、中国科技论文在线优秀期刊、全国理工农医院校社会科学优秀学报、江苏省一级期刊，被美国《剑桥科学文摘》《乌利希国际期刊指南》和中国期刊全文数据库（CJFD）、中国学术期刊综合评价数据库（CAJCED）、国家哲学社会科学学术期刊数据库、万方数据知识服务平台、维普中文期刊服务平台、超星期刊域出版平台、中国科技论文在线等数据库或出版平台列为来源期刊。

合作交流

截至2021年4月，学校拥有招收华侨、港澳台地区学生和外国留学生资格，通过全国高校来华留学质量认证，是国务院侨办华文教育基地，有40多个国家的近800名外国留学生在校学习；先后与36个国家和地区的50余所大学建立合作关系；与爱尔兰梅努斯大学、加拿大圣西维尔大学、美国新泽西城市大学举办5个中外合作办学项目，获批省高校中外合作办学高水平示范性建设工程1项；学校与玻利瓦尔大学共建中国在委内瑞拉的首家孔子学院，承办西班牙安达卢西亚自治区孔子课堂；学校建有跨文化研究院、泰国文化研究中心、拉美研究中心、以色列文化研究中心、中国文化海外传播研究所等智库平台。

帮忙找一篇文章！

Sensorless torque control scheme of
induction motor for hybrid electric vehicle
Yan LIU 1,2, Cheng SHAO1
(ch Institute of Advanced Control Technology, Dalian University of Technology, Dalian Liaoning 116024, China;
of Information Engineering of Dalian University, Dalian Liaoning 116622, China)
Abstract: In this paper, the sensorless torque robust tracking problem of the induction motor for hybrid electric vehicle
(HEV) applications is addressed. Because motor parameter variations in HEV applications are larger than in industrial
drive system, the conventional field-oriented control (FOC) provides poor performance. Therefore, a new robust PI-based
extension of the FOC controller and a speed-flux observer based on sliding mode and Lyapunov theory are developed in
order to improve the overall performance. Simulation results show that the proposed sensorless torque control scheme is
robust with respect to motor parameter variations and loading disturbances. In addition, the operating flux of the motor is
chosen optimally to minimize the consumption of electric energy, which results in a significant reduction in energy losses
shown by simulations.
Keywords: Hybrid electric vehicle; Induction motor; Torque tracking; Sliding mode
1 Introduction
Being confronted by the lack of energy and the increasingly
serious pollution, the automobile industry is seeking
cleaner and more energy-efficient vehicles.A Hybrid Electric
Vehicle (HEV) is one of the solutions. A HEV comprises
both a Combustion Engine (CE) and an Electric Motor
(EM). The coupling of these two components can be in
parallel or in series. The most common type of HEV is the
parallel type, in which both CE and EM contribute to the
traction force that moves the vehicle. Fig1 presents a diagram
of the propulsion system of a parallel HEV [1].
Fig. 1 Parallel HEV automobile propulsion system.
In order to have lower energy consumption and lower pollutant
emissions, in a parallel HEV the CE is commonly
employed at the state (n > 40 km/h or an emergency speed
up), while the electric motor is operated at various operating
conditions and transient to supply the difference in torque
between the torque command and the torque supplied by
the CE. Therefore fast and precise torque tracking of an EM
over a wide range of speed is crucial for the overall performance
of a HEV.
The induction motor is well suited for the HEV application
because of its robustness, low maintenance and low
price. However, the development of a drive system based
on the induction motor is not straightforward because of the
complexity of the control problem involved in the IM. Furthermore,
motor parameter variations in HEV applications
are larger than in industrial drive system during operation
[2]. The conventional control technique ranging from the
inexpensive constant voltage/frequency ratio strategy to the
sophisticated sensorless control schemes are mostly ineffective
where accurate torque tracking is required due to their
drawbacks, which are sensitive to change of the parameters
of the motors.
In general, a HEV operation can be continuing smoothly
for the case of sensor failure, it is of significant to develop
sensorless control algorithms. In this paper, the development
of a sensorless robust torque control system for HEV
applications is proposed. The field oriented control of the induction
motor is commonly employed in HEV applications
due to its relative good dynamic response. However the classical
(PI-based) field oriented control (CFOC) is sensitive to
parameter variations and needs tuning of at least six control
parameters (a minimum of 3 PI controller gains). An improved
robust PI-based controller is designed in this paper,
Received 5 January 2005; revised 20 September 2006.
This work was supported in part by State Science and Technology Pursuing Project of China (No. 2001BA204B01).
Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46 43
which has less controller parameters to be tuned, and is robust
to parameter variable parameters model
of the motor is considered and its parameters are continuously
updated while the motor is operating. Speed and
flux observers are needed for the schemes. In this paper,
the speed-flux observer is based on the sliding mode technique
due to its superior robustness properties. The sliding
mode observer structure allows for the simultaneous observation
of rotor fluxes and rotor speed. Minimization of the
consumed energy is also considered by optimizing operating
flux of the IM.
2 The control problem in a HEV case
The performance of electric drive system is one of the
key problems in a HEV application. Although the requirements
of various HEV drive system are different, all these
drive systems are kinds of torque control systems. For an
ideal HEV, the torque requested by the supervisor controller
must be accurate and efficient. Another requirement is to
make the rotor flux track a certain reference λref . The reference
is commonly set to a value that generates maximum
torque and avoids magnetic saturation, and is weakened to
limit stator currents and voltages as rotor speed increases.
In HEV applications, however, the flux reference is selected
to minimize the consumption of electrical energy as it is one
of the primary objectives in HEV applications. The control
problem can therefore be stated as the following torque and
flux tracking problems:
min
ids,iqs,we Te(t) − Teref (t), (1)
min
ids,iqs,we λdr(t) − λref (t), (2)
min
ids,iqs,we λqr(t), (3)
where λref is selected to minimize the consumption of electrical
energy. Teref is the torque command issued by the
supervisory controller while Te is the actual motor torque.
Equation (3) reflects the constraint of field orientation commonly
encountered in the literature. In addition, for a HEV
application the operating conditions will vary continuously.
The changes of parameters of the IM model need to be accounted
for in control due to they will considerably change
as the motor changes operating conditions.
3 A variable parameters model of induction
motor for HEV applications
To reduce the elements of storage (inductances), the induction
motor model used in this research in stationary reference
frame is the Γ-model. Fig. 2 shows its q-axis (d-axis
are similar). As noted in [3], the model is identical (without
any loss of information) to the more common T-model in
which the leakage inductance is separated in stator and rotor
leakage [3]. With respect to the classical model, the new
parameters are:
Lm = L2
m
Lr
= γLm, Ll = Lls + γLlr,
Rr = γ2Rr.
Fig. 2 Induction motor model in stationary reference frame (q-axis).
The following basic w−λr−is equations in synchronously
rotating reference frame (d - q) can be derived from the
above model.
⎧⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
dλdr
dt
= −ηλdr + (we − wr)λqr + ηLmids,
dλqr
dt
= −(we − wr)λdr − ηλqr + ηLmiqs,
dids
dt
= ηβλdr+βwrλqr−γids+weiqs+
1
σLs
Vds,
diqs
dt
=−βwrλdr+ηβλqr−weids−γiqs+
1
σLs
Vqs,
dwr
dt
= μ(λdriqs − λqrids) −
TL
J
,
dθ
dt
= wr + ηLm
iqs
λdr
= we,
Te = μ(λdriqs − λqrids)
(4)
with constants defined as follows:
μ = np
J
, η = Rr
Lm
, σ = 1−
Lm
Ls
, β =
1
Ll
,
γ = Rs + Rr
Ll
, Ls = Ll + Lm,
where np is the number of poles pairs, J is the inertia of the
rotor. The motor parameters Lm, Ll, Rs, Rr were estimated
offline [4]. Equation (5) shows the mappings between the
parameters of the motor and the operating conditions (ids,
iqs).
Lm = a1i2
ds + a2ids + a3, Ll = b1Is + b2,
Rr = c1iqs + c2.
(5)
4 Sensorless torque control system design
A simplified block diagram of the control diagram is
shown in Fig. 3.
44 Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46
Fig. 3 Control structure.
4.1 PI controller based FOC design
The PI controller is based on the Field Oriented Controller
(FOC) scheme. When Te = Teref, λdr = λref , and
λqr = 0 in synchronously rotating reference frame (d − q),
the following FOC equations can be derived from the equations
(4).
⎧⎪
⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎩
ids = λref
Lm
+ λref
Rr
,
iqs = Teref
npλref
,
we = wr + ηLm
iqs
λref
.
(6)
From the Equation (6), the FOC controller has lower performance
in the presence of parameter uncertainties, especially
in a HEV application due to its inherent open loop
design. Since the rotor flux dynamics in synchronous reference
frame (λq = 0) are linear and only dependent on the
d-current input, the controller can be improved by adding
two PI regulators on error signals λref − λdr and λqr − 0 as
follow
ids = λref
Lm
+ λref
Rr
+ KPd(λref − λdr)
+KId (λref − λdr)dt, (7)
iqs = Teref
npλref
, (8)
we = wr + ηLm
iqs
λref
+ KPqλqr + KIq λqrdt. (9)
The Equation (7) and (9) show that current (ids) can control
the rotor flux magnitude and the speed of the d − q rotating
reference frame (we) can control its orientation correctly
with less sensitivity to motor parameter variations because
of the two PI regulators.
4.2 Stator voltage decoupling design
Based on scalar decoupling theory [5], the stator voltages
commands are given in the form:
⎧⎪
⎪⎪⎨⎪⎪⎪⎩
Uds = Rsids − weσLsiqs = Rsids − weLliqs,
Uqs = Rsiqs + weσLsids + Lm
Lr
weλref
= Rsiqs + weσLsids + weλref .
(10)
Because of fast and good flux tracking, poor dynamics decoupling
performance exerts less effect on the control system.
4.3 Speed-flux observer design
Based on the theory of negative feedback, the design of
speed-flux observer must be robust to motor parameter variations.
The speed-flux observer here is based on the sliding
mode technique described in [6∼8]. The observer equations
are based on the induction motor current and flux equations
in stationary reference frame.
⎧⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
d˜ids
dt
= ηβ˜λdr + β ˜ wr˜λqr − γ˜ids +
1
Ll
Vds,
d˜iqs
dt
= −β ˜ wr˜λdr + ηβ˜λqr − γ˜iqs +
1
Ll
Vqs,
d˜λdr
dt
= −η˜λdr − ˜ wr˜λqr + ηLm
˜i
ds,
d˜λqr
dt
= ˜wr˜λ dr − η˜λqr + ηLm
˜i
qs.
(11)
Define a sliding surface as:
s = (˜iqs − iqs)˜λdr − (˜ids − ids)˜λqr. (12)
Let a Lyapunov function be
V = 0.5s2. (13)
After some algebraic derivation, it can be found that when
˜ wr = w0sgn(s) with w0 chosen large enough at all time,
then ˙V = ˙s · s 0. This shows that s will converge to
zero in a finite time, implying the stator current estimates
and rotor flux estimates will converge to their real values
in a finite time [8]. To find the equivalent value of estimate
wr (the smoothed estimate of speed, since estimate wr is a
switching function), the equation must be solved [8]. This
yields:
˜ weq = wr
˜λ
qrλqr + λdr˜λdr
˜λ
2q
r +˜λ2
dr −
η
np
˜λ
qrλdr − λqr˜λdr
˜λ
2q
r +˜λ2
dr
. (14)
The equation implies that if the flux estimates converge to
their real values, the equivalent speed will be equal to the
real speed. But the Equation (14) for equivalent speed cannot
be used as given in the observer since it contains unknown
terms. A low pass filter is used instead,
˜ weq =
1
1 + s · τ
˜ wr. (15)
Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46 45
The same low pass filter is also introduced to the system
input,which guarantees that the input matches the feedback
in time.
The selection of the speed gain w0 has two major constraints:
1) The gain has to be large enough to insure that sliding
mode can be enforced.
2) A very large gain can yield to instability of the observer.
Through simulations, an adaptive gain of the sliding
mode observer to the equivalent speed is proposed.
w0 = k1 ˜ weq + k2. (16)
From Equation (11), the sliding mode observer structure
allows for the simultaneous observation of rotor fluxes.
4.4 Flux reference optimal design
The flux reference can either be left constant or modified
to accomplish certain requirements (minimum current,
maximum efficiency, field weakening) [9,10]. In this paper,
the flux reference is chosen to maximum efficiency at steady
state and is weaken for speeds above rated. The optimal efficiency
flux can be calculated as a function of the torque
reference [9].
λdr−opt = |Teref| · 4Rs · L2r
/L2
m + Rr. (17)
Equation (17) states that if the torque request Teref is
zero, Equation (8) presents a singularity. Moreover, the
analysis of Equation (17) does not consider the flux saturation.
In fact, for speeds above rated, it is necessary to
weaken the flux so that the supply voltage limits are not exceeded.
The improved optimum flux reference is then calculated
as:
⎧⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪
⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
λref = λdr-opt,
if λmin λdr-opt λdr-rated ·
wrated
wr-actual
,
λref = λmin, if λdr-opt λmin,
λref = λdr-rated ·
wrated
wr-actual
,
if λdr-opt λdr-rated ·
wrated
wr-actual
.
(18)
where λmin is a minimum value to avoid the division by
zero.
4.5 Simulations
The rated parameters of the motor used in the simulations
are given by
Rs = 0.014 Ω, Rr = 0.009 Ω, Lls = 75 H,
Llr = 105 H, Lm = 2.2 mH, Ls = Lls + Lm,
Lr = Llr + Lm, P = 4, Jmot = 0.045 kgm2,
J = Jmot +MR2
tire/Rf, ρair = 1.29, Cd = 0.446,
Af = 3.169 m2, Rf = 8.32, Cr = 0.015,
Rtire = 0.3683 m, M = 3000 kg, wbase = 5400 rpm,
λdr−rated = 0.47 Wb.
Fig.4 shows the torque reference curve that represents
typical operating behaviors in a hybrid electric vehicle.
Fig. 4 The torque reference curve.
Load torque is modeled by considering the aerodynamic,
rolling resistance and road grade forces. Its expression is
given by
TL = Rtire
Rf
(
1
2ρairCdAfv2 +MCr cos αg +M sin αg).
Figures in [5∼8] show the simulation results of the
system of Fig.3 (considering variable motor parameters).
Though a small estimation error can be noticed on the observed
fluxes and speed, the torque tracking is still achieved
at an acceptable level as shown in Figs. [5, 6, 8]. The torque
control over a wide range of speed presents less sensitivity
to motor parameters uncertainty.
Fig.5 presents the d and q components of the rotor flux.
Rotor flux λr is precisely orientated to d-axis because of the
improved PI controllers.
Fig.8 shows clearly the real and observed speed in the
different phases of acceleration, constant and deceleration
speed with the motor control torque of Fig.4. The variable
model parameters exert less influence on speed estimation.
Fig.7 shows the power loss when the rotor flux keeps constant
or optimal state. A significant improvement in power
losses is noticed due to reducing the flux reference during
the periods of low torque requests.
Fig. 5 Motor rotor flux λr.
46 Y. LIU et al. / Journal of Control Theory and Applications 2007 5 (1) 42–46
Fig. 6 Motor torque.
Fig. 7 Power Losses.
Fig. 8 Motor speed.
5 Conclusions
This paper has described a sensorless torque control system
for a high-performance induction motor drive for a
HEV case. The system allows for fast and good torque
tracking over a wide range of speed even in the presence of
motor parameters uncertainty. In this paper, the improved
PI-based FOC controllers show a good performance in the
rotor flux λdr magnitude and its orientation tracking. The
speed-flux observer described here is based on the sliding
mode technique, making it independent of the motor parameters.
Gain adaptation of the speed -flux observer is used to
stabilize the observer when integration errors are present.

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陈独秀（1879年10月9日-1942年5月27日），原名庆同，官名乾生，字仲甫，号实庵，安徽怀宁（今安庆）人。中国近现代史上伟大的爱国者、伟大的革命家与改革家、伟大的民主主义者、伟大的启蒙思想家。他是新文化运动的发起者，是20世纪中国第一次思想解放运动的倡导者。

是五四运动的总司令，是五四运动的思想指导者；是马克思主义的积极传播者；是中国共产党最重要的创始人；是中国共产党第一代领导集体的最主要的领导人；是中国近现代历史上第一个深刻总结、反思苏联和社会主义民主政治建设经验、教训的人。

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字润之，湖南湘潭人。中国人民的领袖，马克思主义者，伟大的无产阶级革命家、战略家和理论家，中国共产党、中国人民解放军和中华人民共和国的主要缔造者和领导人，诗人，书法家。他对马克思列宁主义的发展、军事理论的贡献以及对共产党的理论贡献被称为毛泽东思想。