高超声速飞行器32页PPT
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Acknowledgements
An advanced weapon and space systems company
I gratefully acknowledge my colleagues and sponsors at the following institutions: AFRL ATK GASL ATK Launch Systems DARPA NASA ONR Their work, contributions, and support form the basis of this lecture
X-43A Flight Milestones • Mach 7 Flight: March 27, 2004 • Mach 10 Flight: November 16, 2004
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“NASP Era” Culminates in the Successful X-43A Flights
An advanced weapon and space systems company
An advanced weapon and space systems company
Air Inlet Diffuser / Isolator Combustor
Nozzle
Dual Mode Scramjet (HC-Fuel) Dual Mode Scramjet (HC-Fuel)
• Liquid Jet Fuel (JP-7) – Fuel Cooled • Liquid Jet Fuel (JP-7) – Fuel Cooled • Mach 3.0 to 7.0+ Cruise • Mach 3.0 to 7.0+ Cruise • Long Range Cruise, Time Critical Strike • Long Range Cruise, Time Critical Strike • 1stst Stage Access to Space • 1 Stage Access to Space
高超声速飞行器内外流主动流动控制(罗振兵等著)PPT模板
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6.3.1单丝扭摆测 量系统设计?验证
及精度分析
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6.3.2等离子体高能 合成射流直接力特 性及影响因素研究
第6章高超声 速飞行器快响 应直接力控制
6.4等离子体高能合成射流激 励器飞行器直接力控制数值 仿真
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6.4.1高超声速飞行器外流场仿 真
3.1引言 3.2自持式合成双射流进气道控 制 3.3等离子体高能合成射流进气 道激波调制 3.4小结 参考文献
第3章高超声速飞行器 进气道主动流动控制
3.2自持式合成双射流进气道控 制
A
3.2.1进气道低于设 计马赫数下流场控
制
3.2.2进气道起动 条件下流场控制
B
第3章高超声速飞行器进气道主动流动控制
高超声速飞行器内外流主动流动 控制(罗振兵等著)
演讲人
2 0 2 X - 11 - 11
目 录
0 1 丛书序 0 2 前言 0 3 第1章绪论 0 4 第2章合成射流理论与新型合成射流激励器
0 5 第3章高超声速飞行器进气道主动流动控制
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第4章超燃冲压发动机燃烧室掺混增强主动控制
0 7 第5章高超声速飞行器气动力控制 0 8 第6章高超声速飞行器快响应直接力控制
3.3等离子体高能合成射流进气道激波调制
3.3.1横向等离子体高能合成射流 流场干扰特性
3.3.3高频小能量激励器激波摆动 控制特性
3.3.2低频大能量激励器激波消除 及弱化特性
3.3.4等离子体高能合成射流激波 控制机制研究
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第4章超燃冲压发动机燃 烧室掺混增强主动控制
第4章超燃 冲压发动 机燃烧室 掺混增强 主动控制
高超声速飞行器讲解学习
About Conclusion
About Conclusion
高超音速飞行器的一些特性类似于超音速飞机，但是它仍有许多独特的 特点，使高超音速飞行器的设计特别具有挑战性。高超声速飞机通常具有更 流畅，楔形的几何外形。因为它们在大气中维持高速，因此最小化阻力是重 要的。许多设计也参考了乘波者外形，这样冲击波可以产生额外的升力。而 航天器倾向于更钝，依靠分离的弓形冲击波以尽可能快地减速。
About Aerodynamic Issues
About Hypersonic
我们对两种不同类型的飞行器进行评估：高超 声速运载器和航天器，他们的共有特点和不同之 处将被对比。
高超声速飞行器设计最大的问题之一就是空气 动力学问题。由于飞行器的速度范围非常广，设 计必须满足几个经常会互相矛盾的要求。
About Hypersonic Lift
近年来，有很多解决不同几何形状绕流问 题的方法被发明。例如激波膨胀法。
但是没有任何一种方法普遍适用于任何飞 行器外形，设计师需要对各种方法的基本原 理和基本假设有良好的理解。
左表展示了用于估算高超音速空气动力学 性能的各种压缩和膨胀方法的列表。这些方 法构成SHABP软件的一部分。
然而，我们重点关注的是减少飞行器上升段的 空气阻力（以及高超声速飞机的巡航段）。左图 是多种不同航天器的最大飞行速度。
About Hypersonic
只要发动机动力足够强大，飞行器可以只依靠推 力。无升力（弹道式）飞行器不依赖气动升力，因 此造成了流线型、低阻力外形，但是它们的横向稳 定性和操纵性很差。
----P. L. Roe
About Hypersonic
X-51
Space Ship
Aircraft
吸气式高超声速飞行器控制
经验教训与启示
总结实际案例中的经验教训与启示，为后 续吸气式高超声速飞行器控制系统的设计 与实践提供借鉴与参考。
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未来展望与挑战
吸气式高超声速飞行器控制技术的发展趋势
智能化控制
随着人工智能技术的进步，吸气式高超声速飞行器的控制技术将越来越智能化。先进的算 法和机器学习技术可用于实时决策和优化控制策略，提高飞行器的自主性和适应性。
导航与制导协同优化
综合考虑飞行器性能、任务需求和约束条件，对导航与制 导策略进行协同优化，实现任务成功率和效费比的最大化 。
智能导航与制导
引入人工智能、深度学习等技术，实现导航与制导系统的 自主学习、自适应和自主决策能力，提高复杂环境下的任 务执行能力。
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吸气式高超声速飞行器的 控制系统设计与实践
终端制导
在接近目标时，通过高精度传感器对目标进行捕获和跟踪，实现精 确打击。要求传感器具有高分辨率、快速捕获和抗干扰能力。
复合制导
综合运用多种制导方式，根据不同飞行阶段和任务需求，实现优势互 补，提高制导精度和抗干扰能力。
导航与制导的集成技术
导航与制导信息融合
将不同导航系统和制导方式提供的信息进行有效融合，提 高导航与制导的整体性能。采用卡尔曼滤波、联邦滤波等 信息融合算法进行处理。
控制系统的鲁棒性问题
吸气式高超声速飞行器的控制系统需要具有很高的鲁棒性，以应对各种不确定性因素（如模型误差、外 部干扰等）。提高控制系统的鲁棒性将有助于保证飞行器的安全性和稳定性。
提高吸气式高超声速飞行器控制性能的建议和前景
加强跨学科合作
加大研发投入
建立开放合作机制
吸气式高超声速飞行器控制技术涉及 多个学科领域，包括航空航天、控制 理论、人工智能等。加强跨学科合作 ，促进不同领域专家的交流与合作， 有助于推动控制技术的创新与突破。
080630-高超声速技术研究和发展
MIMI（Module-To-Module)模型是几 个相邻的模块构成，用以确定模块之间 工作的相互影响.至此，模型试验已接近 全部完成。
NASP计划的结束 1994年NASP计划宣布结束，主要原因 有： 经费困难，拨款连年减少； 技术难度大，工作进展慢； NASA 与国会意见分歧。
高超声速冲压发动机 NASP最重要的研究内容是发展从超声速 到高超声速飞行工作的超燃冲压发动机， 开始是进行发动机模型研究，使用1/7 缩比的超声速燃烧冲压发动机。研究了 多种模型,如GBL模型, A—C模型, SX20模型, SXPE和CDE模型, MIMI模型 等.
以上试验验证了发动机流路设计方法， 验证了几何尺寸，动压，试车台气体成 分，粘性效应，附 面层厚度的影响。
（2）在经济上，高超声速武器将提 高作战的实效性。使用空天飞机，将 降低到达地球低轨道的有效载荷发射 费用，可从航天飞机的每公斤有效载 荷一万美元，RLV的每公斤有效载荷 一千美元，降到使用空天飞机的每磅 一百美元，是解决人类进一步开发太 空资源的重要手段，使空间开发更为 现实；同时，提高了安全性和可靠性。
2. 2 NASP计划 1986年2月4日美国宣布推行NASP计划， 研究水平起降，单级入轨的研究机X-30。 NASP计划目的是发展可完全重复使用、单 级入轨、水平起降、超燃冲压发动机推进 的空天飞机。
主要技术问题有： （1）确定在高马赫数的高超声速冲压发动 机特性； （2）确定空天飞机飞行时，由层流附面层 转换为紊流附面层的转捩点； （3）保证空天飞机高超声速飞行时的稳定 性和可操作性。
X-51A计划主要目的
(1) X-43C: X-43C是NASA和空军联合发展的。飞行 器长16英尺，装备三模块冲压发动机。使 用碳氢燃料超燃冲压发动机，并用燃料冷 却。飞行器被加速到马赫5，超燃冲压发动 机启动，然后自行加速到马赫7。飞行持续 5分钟，演示验证飞行性能。该计划的实现 将为发展高超声速巡航导弹创造条件。
飞行器飞行原理ppt课件
2.3 飞机飞行原理
可重复使用的放热材料
用于像航天飞机类似的可重复使用的航天器的防热。 根据航天器表面不同温度的区域，采用相应的可重复使 用的防热材料。
例如：机身头部、机翼前缘温度最高，采用增强碳 碳复合材料，温度可耐受1593度；机身、机翼下表面前 部和垂尾前缘温度高，可采用防热隔热陶瓷材料；机身、 机翼上表面前部和垂尾前缘气动加热不是特别严重处， 可采用防热隔热的陶瓷瓦材料；机身中后部两侧和有效 载荷舱门处，温度相对较低（约350度），可采用柔性的 表面隔热材料；对于温度最高的区域，采用热管冷却和 强制循环冷却和发汗冷却等。
材料来制造飞机的重要受力构件和蒙皮； 2. 用隔热层来保护机内设备和人员； 3. 采用冷却液冷却结构内表面。
美国SR-71的机体结构的93%采用钛合 金越过热障，达到3.3倍音速。
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2.3 飞机飞行原理
航天器的防热方法：
材料：石墨、陶瓷等。 高温下的热解和相变：固 液，固 气，液 气。 应用：烧蚀法适用于不重复使用的飞船、卫星等。
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2.3 飞机飞行原理
B. 超声速飞机的机翼平面形状和布局形式
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2.3 飞机飞行原理
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2.3 飞机飞行原理
F-14 Tomcat 舰载机
米格-23
B-1 Lancer轰炸机
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2.3 飞机飞行原理
边条涡
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2.3 飞机飞行原理
超声速飞机的气动外形
鸭翼产生的脱体漩涡
机翼升力
鸭翼升力 机翼升力
流体黏性和温度有关，气体温度升高，黏性增大。液体相反。
4. 可压缩性
当气体的压强改变时，其密度和体积也改变，为气体可压缩性。 5. 声速
高超声速飞行器
Design and Implementation of Neuro-Fuzzy Vector Control for Wind-Driven Doubly-Fed InductionGeneratorHany M.Jabr,Student Member,IEEE,Dongyun Lu,Student Member,IEEE,andNarayan C.Kar,Senior Member,IEEEAbstract—Wound-rotor induction generators have numerous advantages in wind power generation over other types of gener-ators.One scheme is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power.This configuration is called the doubly-fed induction generator(DFIG).In this paper,a vector control scheme is devel-oped to control the rotor side voltage source converter that allows independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point.A neuro-fuzzy gain tuner is proposed to control the DFIG.The input for each neuro-fuzzy system is the error value of generator speed,active or reactive power.The choice of only one input to the system simplifies the design.Experimental investigations have also been conducted on a laboratory DFIG to verify the calculated results.Index Terms—Doubly-fed induction generator(DFIG),neuro-fuzzy,vector control,wind power generation.I.N OMENCLATURE,Real and reactive power.,Stator voltage and current.,-and-axis components of the stator voltage.,-and-axis components of the rotor voltage.,-and-axis components of the stator current.,-and-axis components of the rotor current.,-and-axis components of the magnetizingcurrent.,-and-axis components of the statorfluxlinkage.,-and-axis components of the rotorfluxlinkage.,Stator and rotor winding resistances.,Stator and rotor leakage reactances.Manuscript received October14,2010;revised March04,2011;accepted May01,2011.Date of publication June23,2011;date of current version September21,2011.The authors are with the Department of Electrical and Computer Engi-neering,University of Windsor,Windsor,ON,N9B3P4,Canada(e-mail: moham1k@uwindsor.ca;lu1y@uwindsor.ca;nkar@uwindsor.ca).Color versions of one or more of thefigures in this paper are available online at .Digital Object Identifier10.1109/TSTE.2011.2160374Magnetizing reactance.Stator reactance.Supply frequency.Rotor speed.,Proportional and integral gains.Mechanical power delivered by the turbine.Active power delivered by the rotor to theconverter.Reactive power delivered by the rotor to theconverter.Line-side active power.Line-side reactive power.Line-side converter current.Rotor-side converter current.Number of poles.Active power through the transmission line.Reactive power through the transmission line.Capacitance.II.I NTRODUCTIONT HE use of doubly-fed induction generators(DFIGs)is receiving increasing attention for grid-connected wind power generation where the terminal voltage and frequency are determined by the grid itself[1]–[8].One configuration is realized by using back-to-back converters in the rotor circuit and employing vector control.This allows the wind turbine to operate over a wide range of wind speed and,thus,maximizes annual energy production.The750-kW and1.5-MW turbines and the3.6-MW prototypes for offshore applications from GE Wind Energy Systems employ vector control of the DFIG rotor currents which provides fast dynamic adjustment of electro-magnetic torque in the machine[9].Fuzzy logic has been successfully applied to control wind-driven DFIGs in different aspects.In[1],fuzzy logic was used to control both the active,and reactive power generation.In[2]and [3],a fuzzy logic gain tuner was used to control the generator speed to maximize the total power generation as well as to con-trol the active and reactive power generation through the control1949-3029/$26.00©2011IEEEof the rotor side currents as demonstrated in Appendix A.The error signal of the controlled variable was the single variable used as an input to the fuzzy system.In the above-mentioned applications,the design of the fuzzy inference system was com-pletely based on the knowledge and experience of the designer, and on methods for tuning the membership functions(MFs) so as to minimize the output error.To overcome problems in the design and tuning processes of previous fuzzy controllers,a neuro-fuzzy based vector control technique isfirst proposed by the authors to effectively tune the MFs of the fuzzy logic con-troller while allowing independent control of the DFIG speed, active,and reactive power.The proposed neuro-fuzzy vector controller utilizes six neuro-fuzzy gain tuners.Each of the pa-rameters,generator speed,active,and reactive power,has two gain tuners.The input for each neuro-fuzzy gain tuner is chosen to be the error signal of the controlled parameter.The choice of only one input to the system simplifies the design.In this research,the two-axis(direct and quadrature axes)dy-namic machine model is chosen to model the wind-driven DFIG due to the dynamic nature of the application.Since the machine performance significantly depends on the saturation conditions, both mainflux and leakageflux saturations have been consid-ered in the induction machine modeling[9].The machine model is then used to build and simulate a neuro-fuzzy vector con-trolled wind-driven DFIG system.The controllers used in vector control are a set of standard PI controllers with neuro-fuzzy gain schedulers.In these controllers,both proportional and integral gains are scheduled based upon the value of the error signal of the speed,active,or reactive power as discussed above.The neuro-fuzzy systems are designed and trained to provide the best dynamic performance while tracking the wind turbine’s max-imum power point curve.Experimental investigations have also been conducted on a2-kW laboratory DFIG to verify the calcu-lated results.III.P ROPORTIONAL AND I NTEGRAL G AIN T UNERSA.Conventional PI,Adaptive,and Fuzzy Gain Tuners Conventional vector controllers[10]and[11]utilize a PI con-troller withfixed proportional and integral gains,and, determined by the zero/pole placement.Such controllers give a predetermined system response and cannot be changed easily. As the system becomes highly nonlinear,more advanced control schemes are required.In[7],an adaptive controller is proposed by the authors that can schedule both and depending on the value of the error.Different characteristics such as linear, exponential,piece-wise linear,and fourth-order functions,rep-resenting the variation in and as a function of the absolute value of the error are used.The coefficients were selected such that,for the proportional gain,a fast system response with less overshoot and small settling time is obtained.While for the inte-gral gain,it is required to reduce the overshoot and to eliminate the steady state error.It has been found that the performance of the system using the exponential characteristic produces the best system response with less overshoot,less settling time,and steady-state error.In[2],a fuzzy algorithm for tuning these two gains of the PI controller is proposed to produce good controlperformance Fig.1.Neuro-fuzzy gain scheduler for vector control of wind-driven DFIG. when parameter variations take place and/or when disturbances are present.This approach uses fuzzy rules to generate propor-tional and integral gains.The design of these rules is based on a qualitative knowledge,deduced from extensive simulation tests of a conventional PI controller of the system for different values of and,for operating conditions.B.Proposed Neuro-Fuzzy Gain TunerIn the neuro-fuzzy system,a learning method similar to that of neural network is used to train and adjust the parameters of the membership functions.Neuro-adaptive learning techniques provide a method for the fuzzy modeling procedure to learn in-formation about a data set.Then,the parameters of membership functions that best allow the associated fuzzy inference system to track the given input/output data are computed as described in Appendix B.The vector control technique is implemented in Fig.1.As shown in thisfigure,the wind speed is measured in order to determine the set values for both the maximum DFIG output power and the corresponding generator speed in order to track the maximum power curve.These set values are then used to calculate the error signal which is the set value minus its corre-sponding measured actual value.The abso-lute value of the error signal is used to calculate the scheduled proportional and integral gains using the neuro-fuzzy controller for each of the speed,active and reactive power controllers.To apply the vector control to the DFIG system,six neuro-fuzzy gain tuners are trained offline.Two for each of active power,re-active power,and speed controllers.One unit is responsible for tuning the proportional gain and the other for tuning the integral gain.The developed neuro-fuzzy system is afirst-order Sugeno type which has a single input with ten Gaussian distribution membership functions.It has ten if–then rules.A simple struc-ture of the developed neuro-fuzzy system is shown in Fig.2 where the input is the error signal of the controlled variable of speed,active,or reactive power.The training is performed using the hybrid back-propagation algorithm.The training data used are collected from extensive simulations of the vector controllerFig.2.Simple structure of a single unit of the neuro-fuzzy gainscheduler.Fig.3.Training error for the speed neuro-fuzzy gaintuner.Fig.4.Input membership functions for the proportional gain tuners.(a)Active power controller.(b)Speed controller.system with various PI gains so that the trained tuner can tune the PI gains online based on the knowledge of the different PI controllers under different operating conditions.The number of training epochs is set to 45with an error tolerance of .The number of epochs is chosen to be the highest number after which there is no signi ficant reduction in the training error.Fig.3shows the error while training at each epoch for the neuro-fuzzy gain tuner of the speed controller.After the training process,the input membership functions for the neuro-fuzzy proportional gain tuner of both active power and speed are shown in Fig.4.The output membership functions are chosen to be linear;the parameters of the ten linear output membership functions for the speed controller and active and reactive power controllers are listed in Tables I and II,respectively.TABLE IPARAMETERSOF THEL INEAR O UTPUT M EMBERSHIP F UNCTIONS OFTHES PEED C ONTROLLERTABLE IIP ARAMETERS OF THE L INEAROUTPUT M EMBERSHIP F UNCTIONS OFTHEA CTIVE AND R EACTIVE P OWER C ONTROLLERSFig.5.Wind-driven DFIG system con figuration.The proportional and integral gains are inputs to the standard PI controller part of the vector controller to generate the control signals and .Then and along with the stator and rotor angles are used to generate signals for the back-to-back converter.The angle and are calculated as in Appendix A.These angles along with and help evaluate a three-phase stator voltage signal that is sent to a PWM controller to generate switching pulses for the back-to-back converters.IV .S IMULATION R ESULTS FOR S UBSYNCHRONOUS ANDS UPER -S YNCHRONOUS O PERATIONS OF THE DFIG The system considered in this paper is a grid connected wind-driven DFIG with the rotor circuit connected to the grid through back-to-back PWM voltage source converters in a con figuration shown in Fig.5.While the rotor-side converter controls the rotor speed and the active and reactive power output through -and -axis com-ponents of the rotor voltage,and ,by using the neuro-Fig.6.DFIG coupled to the dc motor in the experimental setup.TABLE IIIP ARAMETERS OF THE DFIG U SED IN THE INVESTIGATIONSfuzzy-based vector control strategy outlined previously,the grid side converter is controlled to maintain a constant voltage level across the coupling capacitor as demonstrated in Appendix C.A transformer is usually used in the rotor circuit due to the dif-ferent voltage levels between the stator and the rotor.Also,a filter is utilized to minimize the harmonics injected into the grid due to the switching of the power electronic devices.A.DFIG Used in the InvestigationsThe 2-kW DFIG used for the investigations in this paper is driven by a laboratory dc motor,as shown in Fig.6.The pa-rameters of this machine are presented in Table III.The DFIG parameters have been determined by conducting dc,no-load,and locked-rotor tests on the machine.These tests are explained in the IEEE standard of Test Procedure for Poly-Phase Induc-tion Motors and Generators [12]and produce unsaturated ma-chine parameters.In order to obtain a more realistic representa-tion of the machine,saturation in the magnetic circuit along the main and leakage flux paths should be included in the machine model.To determine the saturation characteristics of the magne-tizing,stator and rotor leakage inductances,two unconventional tests are carried out.These test procedures are explained in de-tail in [13].The no-load generator test at synchronous speed is carried out to determine the main flux saturation characteris-tics in Fig.7(a).The terminal voltage–armature current curve with the machine unloaded and unexcited,and the open-circuit characteristics are determined twice;one on the stator and the other on the rotor,to determine the stator and rotor leakage in-ductance saturation characteristics in Fig.7(b)and (c),respec-tively.This main flux path saturation has been represented in the generator model by modifying the unsaturated magnetizing inductance corresponding to the magnetizing current using Fig.7(a).In order to take the leakage flux saturation into ac-count,the unsaturated stator and rotor leakage inductances in the machine model have been modi fied employing the stator and rotor leakage inductance saturation characteristics in Fig.7(b)and (c),respectively.B.Maximum Power Point TrackingThe output power changes as a function of the wind speed as well as the generator speed as shown in Fig.8.To track the max-imum power point,a lookup table is generated based on Fig.8for wind speeds less than 12m/s and saved into the neuro-fuzzy vector controller.Wind speeds higher than 12m/s are beyond the scope of this research.When the measured wind speed changes,the lookup table will be searched to find the set values for both the maximum output power and the generator speed corresponding to the maximum power point.Although measuring the wind speed may have some drawbacks,it is the most accurate and easy way to change the generator speed in order to maximize the power generation.Major wind turbine manufacturers such as Vestas and Nordex have ultrasonic wind sensors in their V90-3.0MW model [14]and N90/2500kW model [15],respectively.The speed infor-mation from the sensor can be used for maximum power point tracking.C.Sub-and Super-Synchronous Operations of the DFIGThe performance of the system employing the proposed neuro-fuzzygaintunerisexaminedunderdifferentoperatingconditions,as shown in Figs.9and 10.Two cases are considered in this paper.The first case investigates the subsynchronous operation where the wind speed changes from 7to 8m/s at s.According to Fig.8,for the maximum power generation of 0.96kW at a wind speedof8m/s,thegeneratorsetspeedincreasesfrom1200to1400rpm.The second case investigates the super-synchronous opera-tion where the wind speed changes from 9to 11m/s at s.The generator set speed increases from 1600to 1900rpm according to the maximum power point curve in Fig.8,where the corre-sponding power output is 1.86kW at a wind speed of 11m/s.For both cases,theproposed neuro-fuzzygain scheduler isemployed.The speed response,stator current,rotor line voltage,and rotor current for subsynchronous operation are shown in Fig.9,while,for the super-synchronous operation,they are shown in Fig.10.V .E XPERIMENTAL D ETERMINATION OF THE DFIG P ERFORMANCE U SING THE P ROPOSED C ONTROLLERS The main objective is to validate the simulation results ob-tained in the previous section as well as investigate the perfor-mance of the DFIG when using different controllers.The types of controllers considered are:adaptive gain scheduler [7],fuzzy logic [2],[3],and neuro-fuzzy.The performance of the DFIG system using the above-mentioned controllers is compared to that of the conventional PI controller with constant gains.While the system stability analysis employing these controllers is not the focus of this paper,the controllers were developed with system stability in mind and it was observed that the system was stable during all experiments.As frequent and rapid changes of the controller gains may lead to instability,there is a limit as to how often and how fast the controller gains can be changed.The conventional PI controller has a proportional gain of 45and an integral gain of 22.5.The DFIG used in this experiment is coupled to a dc motor (Fig.6).The dc motor can be used as a prime-mover in wind turbine applications [10],[11]to adjust speed and deliver the required torque.Fig.7.Saturation characteristics of the DFIG.(a)Magnetizing inductance.(b)Stator leakage inductance.(c)Rotor leakageinductance.Fig.8.Typical wind turbine power curves for different wind speeds showing maximum power point curve.Numerous cases were considered and,for illustration pur-poses,two were chosen and will be demonstrated.For the ease of comparison,these two cases are chosen to be same as the two described in the simulation section.The first case investi-gates the subsynchronous operation of the DFIG with different controllers while the second case investigates the super-syn-chronous operation.A.Hardware ImplementationA practical experimental setup is built using the DFIG system in Fig.6.A power electronics back-to-back voltage source con-verter system is developed that is connected between the rotor circuit and the grid,and a microcontroller is programmed to per-form the controller tasks and calculate the gains depending on the control scheme,whether it is a conventional PI,adaptive,fuzzy,or neuro-fuzzy.The Microchip PIC18F4431microcon-troller was chosen for its high computational performance at an economical price,with the addition of high endurance enhanced flash program memory and a high-speed 10-bit A/D converter.On top of these features,the PIC18F4431introduces design en-hancements that make this microcontroller a logical choice for many high performance motor control applications.LTV-827opto-couplers are used for electrical isolation.Knowing that the working voltage is 208V and the current is a maximum of 8A with a switching frequency of 5kHz,the best choice was MOSFET.The chosen MOSFET is from International Recti-fier (IRF)with a part number IFR740.It is an -channel power MOSFET having a rated voltage of 400V and rated current of 10A.In the case of IRF740,the total gate charge is 63nC with a turn-on time of 41ns which gives a gate current of 1.53 A.Fig.9.Calculated responses for subsynchronous operation.(a)Speed response.(b)Stator current.(c)Rotor line voltage.(d)Rotor current.The selected gate driver is IRS2186from IRF.It is capable of providing 4A for the MOSFET gate.Fig.10.Calculated responses for super-synchronous operation.(a)Speed re-sponse.(b)Stator current.(c)Rotor line voltage.(d)Rotor current.After selecting the components,the next stage is to build the circuit and fabricate the printed circuit board(PCB).Al-tium Designer was used to perform this task for its powerful capabilities in designing the routes and massive component li-brary.All stator and rotor currents are experimentally measured and recorded using a Tektronix TDS1002digital storage oscil-loscope.The sampling frequency is1.25kHz.B.Subsynchronous Operation of the DFIGIn this case,the generator speed changes from1200to 1400rpm.The resulting speed change for each controlleris Fig.11.Measured responses for subsynchronous operation.(a)Speed re-sponse.(b)Stator current.(c)Rotor line voltage.(d)Rotor phase voltage.(e)Rotor current.illustrated in Fig.11(a).It can be seen from thisfigure and Table IV that the conventional PI controller with constant gain has a rise time of2.8s with2%overshoot and1.5%steady-state error while the adaptive PI gain scheduler employing an ex-ponential characteristic has a rise time of2.5s and a settling time of6s with1%overshoot and no steady-state error.On theTABLE IVC ONTROLLERS ’P ERFORMANCE FOR S UBSYNCHRONOUS OPERATIONother hand,the fuzzy PI gain scheduler has a rise time of 2s and a settling time of 3s with no overshoot and no steady-state error while the neuro-fuzzy PI gain scheduler has a rise time of 1.5s and a settling time of 2.8s with no overshoot and no steady-state error.For the same case,the change in speed increased the stator active power production from 0.78to 0.96kW while the reac-tive power produced is kept constant at 0.80kV AR.This rise in power production increased the stator current as shown in Fig.11(b).At the rotor side,the increase in speed required an increase in the rotor current and rotor line voltage which are shown in Fig.11(c)–(e),respectively.In order to increase the active power generation from 0.78to 0.96kW,an increase in the -axis component of the rotor current is required.How-ever,there is no change in the -axis component of the rotor current since the reactive power generation has been kept constant.This increase in has resulted in an increase in the rotor current.C.Super-Synchronous Operation of the DFIGIn the second case,the generator speed changes from 1600to 1900rpm.The resulting speed change for each controller is illustrated in Fig.12(a).It can be seen from this figure and Table V that the conventional PI controller with constant gain has a rise time of 3.8s and a settling time of 4.5s with no over-shoot and no steady-state error.The adaptive PI gain scheduler with an exponential characteristic also yielded similar results.On the other hand,the fuzzy PI gain scheduler has a rise time of 2.5s and a settling time of 4.5s with 0.6%overshoot and no steady-state error while the neuro-fuzzy PI gain scheduler has a rise time of 2.8s and a settling time of 4.5s with no over-shoot and steady-state error.Although the fuzzy PI gain sched-uler has a shorter rise time than that of the neuro-fuzzy PI gain scheduler in this case,it has the largest overshoot among the compared gain scheduler.For this case,the change in speed in-creased the stator active power production from 1.32to 1.86kW while the reactive power production is kept constant at 1kV AR.This increase in power production increased the stator current as shown in Fig.12(b).At the rotor side,the increase in speed required an increase in the rotor current and rotor line voltage which are shown in Fig.12(c)–(e),parison of the ResultsIt can be seen from Figs.9and 10that the speed and stator and rotor quantities calculated by employing the neuro-fuzzy controller in Section IV-C are in good agreement with the exper-imentally measured ones shown in Figs.11to 12.This indicates the accuracy of the proposed control system.The neuro-fuzzy PI gain scheduler enables proportional and integral gains within the vector control scheme to be changed depending on the op-erating conditions.It can also be seen that using theproposedFig.12.Measured responses for super-synchronous operation.(a)Speed response.(b)Stator current.(c)Rotor line voltage.(d)Rotor phase voltage.(e)Rotor current.neuro-fuzzy controller,the dynamic response can be improved and more precise control is achieved in comparison to PI,adap-tive,and fuzzy logic controllers.The proposed neuro-fuzzy PI gain scheduler achieved faster system response with almost no overshoot,shorter settling time and no steady-state error.TABLE VC ONTROLLERS ’P ERFORMANCE FOR S UPER -S YNCHRONOUS OPERATIONFor DFIG operation in the entire speed range,it is well un-derstood that the speed and frequencies of induction machines are such that the stator frequency is equal to the rotor electrical speed combined with the rotor frequency.It can be seen from Figs.10and 12that the relation holds true for the super-syn-chronous mode of operation.Referring to Figs.9(a)and (b)and 11(a)and (b),for subsynchronous mode,it is seen that the rotor speed settles to the new set point of 1400rpm approximately at 4s and the stator current has constant frequency of 60Hz during the entire event;however,Figs.9(c)and (d)and 11(c)and (d)show that the frequency of the rotor quantities decrease even after the speed has settled.Current research is underway to iden-tify and resolve this apparent discrepancy.VI.C ONCLUSIONThis paper presents a control method to maximize power gen-eration of a wind-driven DFIG considering the effect of satura-tion in both main and leakage flux paths.This is achieved by ap-plying vector control techniques with a neuro-fuzzy gain sched-uler.The overall DFIG system performance using the proposed neuro-fuzzy gain tuner is compared to that using the conven-tional PI controllers.The generator speed response as well as the stator and rotor currents and the rotor voltages in response to a sudden change in the wind speed are presented.The main findings of the paper can be summarized in the following points:1)Traditional vector control schemes that employ a conven-tional PI controller with fixed proportional and integral gains give a predetermined response and cannot be changed.However,the proposed neuro-fuzzy PI gain scheduler enables proportional and integral gains within the vector control scheme to be changed depending on the operating conditions.2)It is demonstrated that,using the proposed controller,the system response can be improved and more precise control is achieved.3)The proposed neuro-fuzzy PI gain scheduler achieves faster system response with almost no overshoot,shorter settling time,and no steady-state error.A PPENDIXA)Vector Control:The vector control technique allows decoupled or independent control of both active and reactive power.This section reviews the basic vector control strategy in the case of DFIG.The stator flux oriented rotor current control,with decoupled control of active and reactive power,is adapted in this paper.The control schemes for the DFIG are expected to track a prescribed maximum power curve for maximum powercapturing and to be able to control the reactive power genera-tion.The total active and reactive power generated can be ob-tained from the stator voltage and the -and -axis components of the stator current and can be expressed as(A1)The field oriented control is based on the -axis modeling,where the reference frame rotates synchronously with respect to the stator flux linkage.The direct axis of the reference frame overlaps the axis of stator flux making the -axis component of stator flux .In such a case,the following expression is obtained:(A2)Using (A2)and the active power equation in (A1),the equation of the active power can be rewritten as follows:(A3)The -axis component of stator current can be written as [16](A4)Using (A4)and the reactive power in (A1),the equation of the reactive power can be rewritten as follows:(A5)Therefore,it can be seen from (A3)and (A5)that the -axis component of the rotor current can be controlled to regulate the stator reactive power while the -axis component of the rotor current can be controlled to regulate the stator active power.As a result,the control of the stator active power via and the control of the stator reactive power via are essentially decoupled and,thus,a separate decoupler is not necessary to implement field orientation control for the slip power recovery.The calculation of the stator and rotor angles requires infor-mation pertaining to the stator current,rotor speed and machine parameters.The stator flux angle can be calculated from the fol-lowing equation:(A6)The rotor angle can be calculated as(A7)B)ANFIS Architecture:Currently,several neuro-fuzzy networks exist in the literature.Most notable is the adaptive network-based fuzzy inference system (ANFIS)developed by。
吸气式高超声速飞行器控制
节的组合，对误差进行反馈控制，使系统的输出值与期望值尽量接近。
02
根轨迹控制
根轨迹控制是一种基于系统稳定性分析的控制方法，通过改变系统的根
轨迹形状来达到控制目的。
03
频率响应控制
频率响应控制是一种基于系统频率特性的控制方法，通过控制方法
1 2 3
最优控制
最优控制是一种基于数学最优原理的控制方法， 通过寻找最优的控制策略使系统达到最优状态。
国内研究
中国、德国等国家也在开展相关研究，并取得了一些进展。
吸气式高超声速飞行器的应用前景
军事应用
用于快速打击、战略侦察、空中防御等军事领域。
民用应用
用于高速交通、航空运输、空间探索等民用领域。
02
吸气式高超声速飞行器控制原理
飞行动力学基础
牛顿第二定律
描述物体运动和力的关系，对 于吸气式高超声速飞行器，需 要考虑到升力、重力、阻力和
控制原理
基于飞行动力学和控制理论，设计合适的控制策略和算法，实现吸 气式高超声速飞行器的稳定和控制。
控制器设计
根据控制需求和性能要求，设计合适的控制器，如PID控制器、模 糊控制器、神经网络控制器等。
03
吸气式高超声速飞行器控制方法
经典控制方法
01
PID控制
PID控制是一种最常用的经典控制方法，通过比例、积分和微分三个环
推力等。
空气动力学
研究空气与吸气式高超声速飞行器 相互作用的方式，包括气流速度、 压力、温度等对飞行器的影响。
运动方程
描述吸气式高超声速飞行器的运动 状态，包括速度、位置、加速度等 ，根据不同的控制需求，建立相应 的运动方程。
气动特性分析
气动外形设计
第12章 高超声速流动的特殊问题 气体动力学 教学课件
本章概述:物体的飞行速度远远大于周围介质的声速,而且出
现一系列新特征的流动现象称为高超声速流动.高超声速空气 动力学是近代空气动力学的一个分支，它研究高超声速流体 或高温流体的运动规律及其与固体的相互作用。本章内容将 介绍高超声速流动的基础知识，包括高超声速流动的基本特 征，高超声速流动中的激波，高超声速流动中的气体动力、 气动热以及高超声速边界层等问题。
H如=取59γkm、＝T 1 .=42，58并K按、M正激=3波6,关钝系头计体算头T，部2 弓 形6激52波60K后(的考温虑度真，实 气体效应，T 2 11000K),远比太阳表面温度（约6000K）要
高。如果要精确计算激波层的温度，必须计及化学反应的 影响，比热比为常数或γ＝1.4的假设不再有效。由此可见，
本节综述
高超声速流动区别于超声速流动的基本特征为：流场的非线 性性质、薄激波层、熵层、粘性干扰、高温流动和真实气体效 应、严重的气动加热问题以及高空、高超声速流动存在低密度 效应。
对高超声速流动，不仅边界层内有化学反应，而且整个激波层 内都为化学反应流动所控制。
6、 严重的气动加热问题
在超声速中物面附面层内气流受到粘性滞止，气体微团的动能 转变为热能造成壁面附近的气温升高，高温空气将不断向低温 壁面传热，这就是所谓的气动加热现象。对高超声速流，由于 马赫数很高，附面层内贴近物面的气温能达到接近驻点温度的 高温，气动加热变得十分严重。
4、粘性干扰
以高超声速平板边界层为例。高速或高超声速流动具有很大 的动能，在边界层内，粘性效应使流速变慢时，损失的动能部 分转变为气体的内能，这称为粘性耗散，且随之边界层内的温 度升高。这种温度升高控制了高超声速边界层的特征:气体的 粘性系数随温度升高而增大，其结果使得边界层变厚；另外， 边界层内的法向压力p为常数。由状态方程ρ=p/RT可知，温度 增加导致密度减小，对边界层内的质量流而言，密度减小需要 较大的面积，其结果也是使边界层变厚。这两种现象的联合作 用，使得高超声速边界层的增长比低速情形更为迅速。高超声 速流动的边界层较厚,相应的位移厚度也较大，由此对边界层 外的无粘流动将施加较大的影响，使外部无粘流动发生很大改 变，这一改变反过来又影响边界层的增长。这种边界层与外部 无粘流动之间的相互作用称为粘性干扰。粘性干扰对物面的压 力分布有重要影响，由此，对高超声速飞行器的升力、阻力和 稳定性都造成重要影响,同时使物面摩擦力和传热率增大。
超高声速飞行器
一、研究背景X—33研究背景高超音速，指物体的速度超过5倍音速（约合每小时移动6000公里）以上。
高超音速飞行器主要包括3类：高超音速巡航导弹、高超音速飞机以及航天飞机。
它们采用的超音速冲压发动机被认为是继螺旋桨和喷气推进之后的“第三次动力革命”。
飞行器是航空航天技术的核心，是现代科学技术高度综合的产物，高超声速飞行器是航空航天领域的重要研究发展方向。
高超声速飞行器技术的研究过程是促进科学技术总体水平发展进步的过程，对于国家安全利益而言，发展性能优越的高超声速武器愈加必要，各航天军事大国已竞相把高超声速技术作为重点发展的国家战略目标。
在本文的研究中我们主要基于X-33进行进一步研究，X-33 由洛克希德.马丁公司著名的“臭鼬工程队”研制，它是无人驾驶单级入轨可重复使用航天运载飞行器“冒险星”的1/2 比例的原型机，机长20.29 米，机高，翼展5.88 米22.06 米。
X-33具备把11.35吨有效载荷送上国际空间站的能力，基于其内部具有较大空间可以进行进一步配置的能力，我们将基于它进行进一步改造，进一步扩展其处理小型卫星的能力。
NASA研发的X-33飞行器国外的x-33研究现状：X-33的研究背景基于美国于20世纪末想要争霸太空的想法。
1996年6月，NASA举行了一次声势浩大的太空项目招标会。
组织者介绍说，在经过了“挑战者”号爆炸等悲剧事件之后，美国的航天飞机经受住了考验，随后进行的一系列飞行和太空实验都取得了圆满成功。
然而，由于航天飞机设计的局限性，NASA决定开发下一代太空飞机。
太空飞机是NASA的一个长远设想，说得冠冕堂皇一点，它是将来人类太空旅游的主要载具，但说白了，它是美国争霸太空不可或缺的利器。
然而，太空飞机的设计比航天飞机要复杂得多，因此，在正式决定上马之前，NASA决定首先进行无人太空飞机的研制，如试验成功，NASA将上马代号为“冒险明星”的既可以载人又可以载货的超级太空飞机。
美X-51A高超声速飞行器基本情况概要及几次飞行情况简介
美X-51A高超声速飞行器基本情况概要及几次飞行情况简介1简介x-51A是美国空军研究实验室（AFRL）与国防高级研究计划局（DARPA）联合主持研制的超燃冲压发动机验证机——乘波飞行器（SED-WR，Scramjet Engine Demonstrator-Waverider）。
它由波音公司与普拉特〃惠特尼（简称普惠）公司共同开发，由一台JP-7碳氢燃料超燃冲压发动机推动，设计飞行马赫数在6～6.5之间。
这个计划的终极目标就是要发展一种比美国原武器库中任何一种导弹的速度都要快5倍以上，可以在1小时内攻击地球任意位臵目标的新武器。
2012年8月14日，X-51A第3次试飞，从纽约飞到伦敦将只需不到一个小时。
X-51飞行器2研发背景2.1“全球快速打击计划”的推动“全球快速打击计划”提出于20世纪90年代，目的是让美军能在1小时内用常规武器打击地球上的任何目标。
美国的“快速全球打击”计划将分阶段实施，近期实施海军“三叉戟”导弹的常规改装计划，中期实施海军的“潜射全球打击导弹”方案和空军的助推一滑翔式导弹方案，远期实施正在研究的“高超声速巡航导弹”等方案。
该计划的关键在于“速度”，配套研制的各种飞行器都必须达到5倍以上的声速，其中最具代表性的就是X-51。
五角大楼的决策者们念念不忘多年前的一个深刻教训。
1998年8月20日，位于阿拉伯海上的美国“林肯”号航母战斗群发射了数枚“战斧”巡航导弹，攻击阿富汗东部塔利班训练营地，目的是清除本〃拉登。
“战斧”巡航导弹的最大飞行速度为885千米/时，飞行了1770千米，耗时长达2个小时。
结果，拉登在导弹飞抵前一个小时刚刚离开了训练营地。
这次行动的失败给美国国防部留下了无法弥补的遗憾，从而促使了高超音速武器的研制工作开始加速。
2001年9月l1日，美国本土首次遭到恐怖组织的大规模攻击，促使布什政府开始积极调整美国的军事战略，以应对新形势下难以预测和控制的各种威胁。