信息与网络安全已经成为了我们日常生活中无法绕过的话题，面对各种安全隐患和网络攻击，如何保障信息安全，筑牢网络防线已经成为了国家、企业以及个人必须面对的一项重要任务。作为一名互联网安全领域的从业者，我深知网络安全的重要性，下面我将结合当前的网络安全形势，就信息与网络安全问题分享一些看法和建议。
一. 网络安全形势的现状
网络安全形势的变化在于，攻击手法的升级与多样化，网络安全威胁也从一个环节延伸到多个环节，更多的是针对移动终端、物联网、云安全方面的攻击。为了实现对信息安全的保护，我们必须对攻击手法及时做出调整和升级，同时在完善各个环节的同时，加强整个网络生态系统的协同与管理。
1. 多点受攻击
互联网架构的形式决定了万物皆可互连，但网络上的每一个节点都面临着攻击的危险，从而导致多点受到攻击的事件频频发生，尤其是在当前以云计算为主的网络环境下，脆弱性更易被攻击者利用。
2. 黑灰产业链的繁荣
随着网络攻击形式的不断升级，网络黑灰产业链已经形成，各种攻击方式纷至沓来。从网络入侵、数据盗窃，到勒索软件、网络钓鱼、恶意软件甚至是利用物联网设备形成的僵尸网络攻击，对于列入黑名单的目标机器的反复攻击，一些犯罪分子甚至利用租借服务，暂时模拟出真实的攻击行为。
3. 网络空间的战略意义日益彰显
越来越多的国家开始提高对网络空间的安全防护水平，开发针对网络攻击的先进技术，并通过立法和行政手段来加强网络管理。同时，网络犯罪和恐怖主义的渗透方式已不再局限于单一的实体，恶意利益如此深入，形势十分严峻，迫切需要加强网络安全行业的整合及合作。
二. 信息与网络安全的策略
随着信息技术的迅猛发展，网络安全技术也不断升级和完善，从单纯的防火墙、杀毒软件、加密技术，到威胁情报、入侵检测压测、安全评估等多种技术相互结合，积极承担起网络安全维护的责任。为保障信息与网络安全，我们需要在几个方面加强建设和管理：
1. 重视网络安全人才培养
网络安全人才是当前亟需解决的问题之一，通过大力培养网络安全人才，提高人员素质和技能，引导他们支持和企业加强对网络安全的管理和监管，提升整个网络生态系统的安全性。
2. 更加严格的信息管理制度
针对互联网的复杂性和多样性，我们需要适时调整信息管理制度，严格规范信息收集与处理、通信安全、安全保密与备份等内容，保护关键信息不被别有用心的人所窃取、篡改、损坏甚至泄漏。要提高安全意识，切实加强网络安全的管理，建立高效、安全的信息系统与网络。
3. 白帽攻防竞赛和漏洞报告机制
网络安全问题需要由所有参与者共同协作，国内外的黑帽攻击和灰产业链的产生，也就意味着对技术的拓展和对技术本身的发展，提出更高的要求。在这时，白帽攻防竞赛和漏洞报告机制变得尤为重要，它可以促进网络安全，提升技术防护水平，降低网络安全风险。
4. 完善网络安全法规制度
对于技术手段无法满足的安全问题，需要通过立法手段进行解决。国家不断完善网络安全相关法规制度，加强对网络空间的管理和规范，并制定相关奖惩制度。企业个人也应建立完善的安全管理制度，并投入人力、物力、财力，实行全员安全教育和培训。
三. 结语
保障信息与网络安全，筑牢网络防线，需要每一个参与者共同努力，特别是互联网领域的企业和从业者，更需要关注和重视网络安全问题，树立风险意识和安全意识，积极参与网络安全领域的建设，共同打造一个安全、便捷、开放的网络环境。我们相信，在更加开放的大数据时代，保障信息安全的意义更加重大。
相关问题拓展阅读：
- 帮忙找一篇文章！
- 网络安全和信息安全区别？
帮忙找一篇文章！
呵呵，从具体的情况来看，好象这类的问题应该有前侍专厅拆业方面的人才来帮助你回答啊！扮悔枣
很可惜我不会啊！
Sensorless torque control scheme of
induction motor for hybrid electric vehicle
Yan LIU 1,2, Cheng SHAO1
(1.Research Institute of Advanced Control Technology, Dalian University of Technology, Dalian Liaoning, China;
2.School of Information Engineering of Dalian University, Dalian Liaoning, 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 .
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
. 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 oothly
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 2023; revised 20 September 2023.
This work was supported in part by State Science and Technology Pursuing Project of China (No. 2023BA204B01).
Y. LIU et al. / Journal of Control Theory and Applications(1) 42–46 43
which has less controller parameters to be tuned, and is robust
to parameter variation.The 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 , 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 . 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+
σLs
Vds,
diqs
dt
=−βwrλdr+ηβλqr−weids−γiqs+
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 =
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 . 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(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 , 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 . 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 +
Ll
Vds,
d˜iqs
dt
= −β ˜ wr˜λdr + ηβ˜λqr − γ˜iqs +
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 . To find the equivalent value of estimate
wr (the oothed estimate of speed, since estimate wr is a
switching function), the equation must be solved . 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 + s · τ
˜ wr. (15)
Y. LIU et al. / Journal of Control Theory and Applications(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) . 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 .
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
ysis 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
(
2ρairCdAfv2 +MCr cos αg +M sin αg).
Figures in show the simulation results of the
system of Fig.3 (considering variable motor parameters).
Though a all 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. . 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(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.
Robotics education in the university*
Rafael M. Inigo and Jose M. Angulo
School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia 22901, USA
Dept. de Informatica, Universidad de Deusto, Bilbao, Spain
Available online 28 October 2023.
The importance of automation and robotics in modern factories has required the introduction of courses on these subjects at the graduate and undergraduate levels in engineering schools. A comprehensive course on robotics must include the following subjects of fundamental importance: kinematics, dynamics, computer hardware and software, automatic control and machine vision. This paper describes the authors’ experience in teaching a graduate robotics course at the University of Virginia and a short summer course at the Universidad de Deusto in Spain. Hands-on experience is a must in courses on robotics, and some simple yet effective systems designed and constructed by students are described. These include a program for transformation matrix manipulation, an operating system for manipulator control, and a simple three degrees of freedom programmable manipulator. The majority of the students who took both courses were electrical engineers, but mechanical engineers and computer scientists were also enrolled.
Author Keywords: Robotics Education; Robotics Laboratory; Hardware; Software Development For Robotics Education
*Parts of this paper were presented at the Second annual workshop on interactive computing, CAD/CAM: Electrical Engineering Education Washington,
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英语写作网上可能会有
网络安全和信息安全区别？
信息安全与网络安全的区别和联系，信息安全不仅限于网络，包括人、财、物等信息安全。
（1）信息安全的有关概念。信息安全（InformationSecurity）是指系统的硬件、软件及其信息受到保护，并持续正常地运行和服务。信息安全的实质是保护信息系统和信息资源免受各种威胁、干扰和破坏，即保证信息的安全性。主要目标是防止信息被非授泄露、更改、破坏或被非法的系统辨识与控制，确保信息的保密性、完整性、可用性、可控性和可审查性（信息安全5大特征）。在《计算机信息系统安全保护笑猛条例》中指出，计算机信息系统的安全保护，应当保障计算羡升袭机及其相关的配套设备、设施（含网络）的安全，运行环境的安全，保障信息的安全，保障计算机功能的正常发挥，以维护计算机信息系统安全运行。
国际标准化组织（ISO）对于信息安全给出的定义是：为数据处理系统建立和采取的技术及管理保护，保护计算机硬件、软件、数据不因偶然及恶意的原因而遭到破坏、更改和泄漏。
（2）网络安全与网络空间安全的概念。网络安全指利用网络技术、管理和控制等措施，保证网络系统和信息的保密性、完整性、可用性、可控性和可审查性受到保护。即保证网络系统的硬件、软件及系统中的数据资源得到完整、准确、连续运行与服务不受干扰破坏和非授使兄兄用。
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