3.32 In aircraft control systems, an ideal pitch response ( qo) versus a pitch command ( qc) is described by the transfer function



Similar documents
Gravitation. Definition of Weight Revisited. Newton s Law of Universal Gravitation. Newton s Law of Universal Gravitation. Gravitational Field

PCA vs. Varimax rotation

Chapter 30: Magnetic Fields Due to Currents

Standardized Coefficients

Parameter Identification of DC Motors

Electric Potential. otherwise to move the object from initial point i to final point f

Effect of Unemployment Insurance Tax On Wages and Employment: A Partial Equilibrium Analysis

Skills Needed for Success in Calculus 1

Chapter 4: Matrix Norms

Perturbation Theory and Celestial Mechanics

Symmetric polynomials and partitions Eugene Mukhin

Worked Examples. v max =?

Mechanics 1: Work, Power and Kinetic Energy

Vector Calculus: Are you ready? Vectors in 2D and 3D Space: Review

Orbit dynamics and kinematics with full quaternions

CHAPTER 10 Aggregate Demand I

PERRON FROBENIUS THEOREM

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)

Week 3-4: Permutations and Combinations

The force between electric charges. Comparing gravity and the interaction between charges. Coulomb s Law. Forces between two charges

Coordinate Systems L. M. Kalnins, March 2009

Questions & Answers Chapter 10 Software Reliability Prediction, Allocation and Demonstration Testing

Bending Stresses for Simple Shapes

Model Question Paper Mathematics Class XII

Gauss Law. Physics 231 Lecture 2-1

Forces & Magnetic Dipoles. r r τ = μ B r

(Semi)Parametric Models vs Nonparametric Models

NURBS Drawing Week 5, Lecture 10

2. TRIGONOMETRIC FUNCTIONS OF GENERAL ANGLES

Keywords: Transportation network, Hazardous materials, Risk index, Routing, Network optimization.

UNIT CIRCLE TRIGONOMETRY

Problem Set # 9 Solutions

Voltage ( = Electric Potential )

4a 4ab b (count number of places from first non-zero digit to

Spirotechnics! September 7, Amanda Zeringue, Michael Spannuth and Amanda Zeringue Dierential Geometry Project

AN IMPLEMENTATION OF BINARY AND FLOATING POINT CHROMOSOME REPRESENTATION IN GENETIC ALGORITHM

LINES ON BRIESKORN-PHAM SURFACES

Moment and couple. In 3-D, because the determination of the distance can be tedious, a vector approach becomes advantageous. r r

Derivation of Humidty and NOx Humidty Correction Factors

THE PRINCIPLE OF THE ACTIVE JMC SCATTERER. Seppo Uosukainen

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

Chapter 19: Electric Charges, Forces, and Fields ( ) ( 6 )( 6

Exam 3: Equation Summary

A project management support tool using communication for agile software development

Efficient Evolutionary Data Mining Algorithms Applied to the Insurance Fraud Prediction

FXA Candidates should be able to : Describe how a mass creates a gravitational field in the space around it.

The OC Curve of Attribute Acceptance Plans

Lecture 29. Operational Amplifier frequency Response. Reading: Jaeger 12.1 and Notes

Displacement, Velocity And Acceleration

Carter-Penrose diagrams and black holes

Solutions to Sample Problems for Test 3

Solutions to Problems: Chapter 7

Chapter 3 Savings, Present Value and Ricardian Equivalence

LATIN SQUARE DESIGN (LS) -With the Latin Square design you are able to control variation in two directions.

Anais III Simpósio Regional de Geoprocessamento e Sensoriamento Remoto Aracaju/SE, 25 a 27 de outubro de 2006

Purchase and rental subsidies in durable-good oligopolies* 1

Recurrence. 1 Definitions and main statements

FI3300 Corporate Finance

Physics 110 Spring D Motion Problems: Projectile Motion Their Solutions

PY1052 Problem Set 8 Autumn 2004 Solutions

Voltage ( = Electric Potential )

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t

THE ANALYSIS OF MERGERS THAT INVOLVE MULTI-SIDED PLATFORM BUSINESSES

I = Prt. = P(1+i) n. A = Pe rt

A New replenishment Policy in a Two-echelon Inventory System with Stochastic Demand

Physics 235 Chapter 5. Chapter 5 Gravitation

Level Annuities with Payments Less Frequent than Each Interest Period

Delft. Matlab and Simulink for Modeling and Control. Robert Babuška and Stefano Stramigioli. November 1999

Continuous Compounding and Annualization

Optimizing Supply Chain Collaboration Based on Negotiation and Bargain Power for Single Retailer And Single Supplier

Scal abil it y of ANSYS 16 applicat ions and Hardware select ion.

Magnetic Field and Magnetic Forces. Young and Freedman Chapter 27

Graphs of Equations. A coordinate system is a way to graphically show the relationship between 2 quantities.

Chapter 22. Outside a uniformly charged sphere, the field looks like that of a point charge at the center of the sphere.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

s-domain Circuit Analysis

On Some Functions Involving the lcm and gcd of Integer Tuples

SIMPLE LINEAR CORRELATION

An Algorithm For Factoring Integers

Order-Degree Curves for Hypergeometric Creative Telescoping

EXAMPLE PROBLEMS SOLVED USING THE SHARP EL-733A CALCULATOR

Lecture 3: Force of Interest, Real Interest Rate, Annuity

The Binomial Distribution

AN EQUILIBRIUM ANALYSIS OF THE INSURANCE MARKET WITH VERTICAL DIFFERENTIATION

Lecture 16: Color and Intensity. and he made him a coat of many colours. Genesis 37:3

Risk Sensitive Portfolio Management With Cox-Ingersoll-Ross Interest Rates: the HJB Equation

Competitive Targeted Advertising with Price Discrimination

Using Model Checking to Analyze Network Vulnerabilities

Gravitation and Kepler s Laws Newton s Law of Universal Gravitation in vectorial. Gm 1 m 2. r 2

Personal Saving Rate (S Households /Y) SAVING AND INVESTMENT. Federal Surplus or Deficit (-) Total Private Saving Rate (S Private /Y) 12/18/2009

Additional File 1 - A model-based circular binary segmentation algorithm for the analysis of array CGH data

Transcription:

. In acaft contol ytem, an deal ptch epone ( qo) veu a ptch command ( qc) decbed by the tanfe functon Q () τωn ( + / τ ) Qc() + ζωn+ ωn The actual acaft epone moe complcated than th deal tanfe functon; nevethele, the deal model ued a a gude fo autoplot degn. Aume that t the deed e tme, and that.789 ωn t.6 τ t ζ.89 Show that th deal epone poee a fat ettlng tme and mnmal ovehoot by plottng the tep epone fo t.8,.,., and.5 ec. Soluton: The followng pogam tatement n MATLAB poduce the followng plot: % Poblem. t [.8...5]; t[:4]/; tbackflpl(t); clf; fo I:4, wn(.789)/t(i); %Rad/econd taut(i)/(.6); %tau zeta.89; % btau*(wn^)*[ /tau]; a[ *zeta*wn (wn^)]; ytep(b,a,t); ubplot(,,i); plot(t,y); ttletextpntf('t%.f econd',t(i)); ttle(ttletext); xlabel('t (econd)'); ylabel('qo/qc'); ymax(max(y)-)*; mgpntf('max ovehoot%.f%%',ymax); text(.5,.,mg); ybackflpud(y); yndfnd(ab(yback-)>.); ttback(mn(ynd)); mgpntf('settlng tme %.f ec',t); text(.5,.,mg); gd; end

.4 t.8 econd.4 t. econd.. Qo/Qc.8.6 Qo/Qc.8.6.4 Max ovehoot.%. Settlng tme. ec 4 6 8 t (econd).4 Max ovehoot.%. Settlng tme.9 ec 4 6 8 t (econd).4 t. econd.4 t.5 econd.. Qo/Qc.8.6 Qo/Qc.8.6.4 Max ovehoot.%. Settlng tme.5 ec 4 6 8 t (econd).4 Max ovehoot.%. Settlng tme 4.4 ec 4 6 8 t (econd).5 Conde the two nonmnmum phae ytem, ( ) G ( ) ( + )( + ) ( )( ) G ( ) ( + )( + )( + ) (a) Sketch the unt tep epone fo G ( ) and G ( ), payng cloe attenton to the tanent pat of the epone. (b) Explan the dffeence n the behavo of the two epone a t elate to the zeo locaton. (c) Conde a table, tctly pope ytem (that, m zeo and n pole, whee m< n). Let yt ( ) denote the tep epone of the ytem. The tep epone ad to have an undehoot f t ntally tat off n the "wong" decton. Pove that a table, tctly pope ytem ha an undehoot f and only f t tanfe functon ha an odd numbe of eal RHP zeo.

Soluton: ( a) Fo G ( ) : ( ) Y( ) G( ) ( + )( + ) H( ) k ( z ) ( p ) R lm[( p ) H( )] lm k p l p p l ( z ) ( p z ) k ( p ) ( p p ) Each facto ( p z ) o ( p p ) can be thought of a a complex numbe l l l l l (a magntude and a phae) whoe pctoal epeentaton a vecto pontng to p and comng fom z o p epectvely. l The method fo calculatng the edue at a pole p () Daw vecto fom the et of the pole and fom all the zeo to the pole. () Meaue magntude and phae of thee vecto. () The edue wll be equal to the gan, multpled by the poduct of the vecto comng fom the zeo and dvded by the poduct of the vecto comng fom the pole. In ou poblem: Y( ) ( ) R R R 4 ( + )( + ) + + + + + + + t y ( t) 4e + e t Step Repone fo G : p.8.6.4 y(t). -. -.4 4 5 6 7 8 9 Tme (ec)

Fo G ( ) : ( )( ) 9 8 Y ( ) + + + ( + )( + )( + ) + + + y ( t) 9e + 8e e t t t Step Repone fo G.8.6.4 y(t). -. -.4 4 5 6 7 8 9 Tme (ec) (b) The ft ytem peent an "undehoot". The econd ytem, on the othe hand, tat off n the ght decton. The eaon fo th ntal behavo of the tep epone wll be analyzed n pat c. t In y( t): domnant at t the tem 4e t In y( t): domnant at t the tem 8e (c) The followng conce poof fom [] (ee alo []-[]). Wthout lo of genealty aume the ytem ha unty DC gan ( G() ). Snce the ytem table, y( ) G(), and t eaonable to aume y( ). Let u denote the pole-zeo exce a n m. Then, y( t) and t devatve ae zeo at t, and y () the ft non-zeo devatve. The ytem ha an undehoot f y () y( ) <. The tanfe functon may be e-wtten a m ( ) z G ( ) m+ ( ) p The numeato tem can be clafed nto thee type of tem: (). The ft goup of tem ae of the fom ( α) wth α >. (). The econd goup of tem ae of the fom ( + α ) wth α >. 4

(). Fnally, the thd goup of tem ae of the fom, ( + β+ α ) wth α >, and β could be negatve. Howeve, β < 4 α, o that the coepondng zeo ae complex. All the denomnato tem ae of the fom (), (), above. Snce, y () lm G( ) t een that the gn of y () detemned entely by the numbe of tem of goup above. In patcula, f the numbe odd, then y () negatve and f t even, then y () potve. Snce y( ) G(), then we have the deed eult. [] Vdyaaga, M., "On Undehoot and Nonmnmum Phae Zeo," IEEE Tan. Automat. Cont., Vol. AC-, p. 44, May 986. [] Clak, R., N., Intoducton to Automatc Contol Sytem, John Wley, 96. [] Mta, T. and H. Yohda, "Undehootng phenomenon and t contol n lnea multvaable evomechanm, " IEEE Tan. Automat. Cont., Vol. AC-6, pp. 4-47, 98..9 Suppoe that unty feedback to be appled aound the followng open-loop ytem. Ue Routh' tablty cteon to detemne whethe the eultng cloed-loop ytem wll be table. 4( + ) ( a) KG( ) + + + ( + 4) ( b) KG( ) ( + ) ( c) KG( ) Soluton: ( 4) + + + ( ) 4( ) + KG( ) 4( + ) ( a) T( ), a( ) + KG( ) + + + 8 + 8 4 + + + 4 4 a b : 8 : 8 : : c : d 8 8 whee a, b 8, b 8a c 4, d b 8 a gn change n ft column oot not n LHP untable. 8 + 8 5

KG( ) ( + 4) b T a + KG( ) + + + 8 ( ) ( ), ( ) + + + 8 The Routh' aay, : : 8 : 6 : 8 Thee ae two gn change n the ft column of the Routh aay. Theefoe, thee ae two oot not n the LHP..4 Ue Routh' tablty cteon to detemne how many oot wth potve eal pat the followng equaton have. 5 4 ( b) + + + 8 + 44+ 48 ( d) + + + 78 Soluton: 5 4 ( b) + + + 8 + 44+ 48 5 : 44 4 : 8 48 : 48 ( ) : 5 44 ( ) 5 : ( ) 4 : ( ) 44 oot not n LHP. ( d) + + + 78 : : 78 : ( ) 58 : ( ) 78 oot not n LHP. 6

.4 Fnd the ange of K fo whch all the oot of the followng polynomal ae n the LHP. + 5 + + + 5 + 5 4 K Ue MATLAB to vefy you anwe by plottng the oot of the polynomal n the -plane fo vaou value of K. + + + + + K 5 4 Soluton: 5 5 5 4 : 5 : 5 K : a a : b K : c : K ( ) 5() ( K) 5(5) 5 K Whee a 8, a 5 5 5 5a a 55+ K b, a 8 Ka ab K + 5K 75 c, b 5( K + 55) Fo tablty: all tem n ft column > 55 + K () b > 8 K > 55 K + 5K 75 ( K.89)( K + 54) () c >, < 5( K + 55) 5( K + 55) 55 < K <.89 () K > Combnng (), (), and () < K <.89. If we plot the oot of the polynomal fo vaou value of K we obtan the followng oot locu plot (ee Chapte 5), 7

A: Ue Matlab to compute the ovehoot, e tme, ettlng tme (wth epect to tep epone) ( +.5α ) fo ytem H ( ) when α, 4,,, epectvely. Plot the tme.5α ( + + ) epone fo each cae and compae the eult. Chooe the fnal tme a 5 econd. Be caeful that when mall, the tep epone may co the lne y.9 eveal tme. Soluton: % Poblem 5.A lnepec['','g','b','k']; alpha [ 4 ]; mp[]; t[]; t[]; t[:.:5]; clf; fo :4 b[.5*alpha()]; a.5*alpha()*[ ]; ytep(b,a,t); plot(t,y,lnepec()); hold on; % Ovehhoot 8

mp[mp (max(y)-)*]; % Rng Tme yndfnd(y>.); tt(mn(ynd)); yndfnd(y>.9); tt(mn(ynd)); t[t t-t]; % Settlng Tme yndfnd(ab(y-)>.); t[t t(max(ynd))]; end xlabel('t (econd)'); ylabel('step Repone y(t)'); legend(pntf('\\alpha %d, mp %.f%%, t %.f, t %.f', alpha(), mp(), t(), t()),... pntf('\\alpha %d, mp %.f%%, t %.f, t %.f', alpha(), mp(), t(), t()),... pntf('\\alpha %d, mp %.f%%, t %.f, t %.f', alpha(), mp(), t(), t()),... pntf('\\alpha %d, mp %.f%%, t %.f, t %.f', alpha(4), mp(4), t(4), t(4))); gd;.8.6.4 α, mp 6.7%, t.6, t 8.6 α 4, mp 9.%, t.4, t 8. α, mp 9.8%, t.9, t 7.9 α, mp 69.9%, t.5, t. Step Repone y(t)..8.6.4. 5 5 t (econd) 9