CDS 101: Lecture 5.1 Controllability and State Space Feedback

Size: px
Start display at page:

Download "CDS 101: Lecture 5.1 Controllability and State Space Feedback"

Transcription

1 CDS, Lecture 5. CDS : Lecture 5. Cotrollability ad State Space Feedback Richard M. Murray 8 October Goals: Deie cotrollability o a cotrol system Give tests or cotrollability o liear systems ad apply to eamples Describe the desig o state eedback cotrollers or liear systems Readig: Packard, Poola ad Horowitz, Dyamic Systems ad Feedback, Sectio 5 Optioal: Friedlad, Sectios (cotrollability oly Optioal: A. D. Lewis, A Mathematical Itroductio to Feedback Cotrol, Chapter (available o web page Lecture Review 4.: rom Liear Last Systems Week u = A + Bu y = C+ ( = y t ( = At A( t τ ( + ( τ τ + ( τ = yt Ce Ce Bu d t Properties o liear systems Liearity with respect to iitial coditio ad iputs Stability characterized by eigevalues May applicatios ad tools available Provide local descriptio or oliear systems 8 Oct CDS /8/

2 CDS, Lecture 5. Cotrol Desig Cocepts System descriptio: sigle iput, sigle output oliear system (MIMO also OK = (, u, ( give y = h(, u Stability: stabilize the system aroud a equilibrium poit Give equilibrium poit e, id cotrol law u=α( such that lim t ( = or all ( t e Cotrollability: steer the system betwee two poits Give,, id a iput u(t such that = u t t = T = (, ( takes ( ( Trackig: track a give output trajectory Give y d (t, id u=α(,t such that ( lim yt ( y( t = or all ( t d y(t y d (t t 8 Oct CDS 3 Cotrollability o Liear Systems = A + Bu y = C+, ( give De A liear system is cotrollable i or ay, ad ay time T > there eists a iput u:[,t] such that the solutio o the dyamics startig rom (= ad applyig iput u(t gives (T=. Remarks I the deiitio, ad do ot have to be equilibrium poits we do t ecessarily stay at ater time T. Cotrollability is deied i terms o states does t deped o output Ca characterize cotrollability by lookig at the geeral solutio o a liear system: T AT A( T τ ( T = e + e Bu( τ dτ τ = I itegral is ull rak, the we ca id a iput to achieve ay desired ial state. 8 Oct CDS 4 /8/

3 CDS, Lecture 5. Tests or Cotrollability Thm A liear system is cotrollable i ad oly i the cotrollability matri is ull rak. = A + Bu y = C+, ( give B AB A B A B T ( = AT A( T τ + ( τ τ = T e e Bu dτ Remarks Very simple test to apply. I MATLAB, use ctrb(a,b I this test is satisied, we say the pair (A,B is cotrollable Some isight ito the proo ca be see by epadig the matri epoetial = + ( + ( + + ( + B ABT ( τ ABT ( τ A BT ( τ AT ( τ e B I A T τ A T τ A T τ B = Oct CDS 5 Eample #: Liearized pedulum o a cart θ m Questio: ca we locally cotrol the positio o the cart by proper choice o iput? Approach: look at the liearizatio aroud the upright positio (good approimatio to the ull dyamics i θ remais small M 8 Oct CDS 6 /8/ 3

4 CDS, Lecture 5. Eample #, co t: Liearized pedulum o a cart θ m M Cotrollability matri B AB A B A 3 B Full rak as log as costats are such that colums ad 3 are ot multiples o each other cotrollable as log as g(m+m ca steer liearizatio betwee poits by proper choice o iput 8 Oct CDS 7 Costructive Cotrollability Give that system is cotrollable, how do we id iput to traser betwee states? Simple case: chai o itegrators 3 = = = = u Fid curve (t such that ( ( = ( ( = ( T ( T ( ( T Choose iput as ut ( = ( t ( Cotrollable caoical orm I cotrollable, ca show there eists a liear chage o coordiates such that z = z+ u a a a z z z = ( z ut ( = z ( t + ( az ( + + az 8 Oct CDS 8 /8/ 4

5 CDS, Lecture 5. Cotrol Desig Cocepts System descriptio: sigle iput, sigle output oliear system (MIMO also OK = (, u, ( give y = h(, u Stability: stabilize the system aroud a equilibrium poit Give equilibrium poit e, id cotrol law u=α( such that lim t ( = e or all ( t Cotrollability: steer the system betwee two poits Give,, id a iput u(t such that = (, u( t takes ( t = ( t = Trackig: track a give output trajectory Give y r (t, id u=α(,t such that ( lim yt ( y( t = or all ( t d 8 Oct CDS 9 y(t y d (t t State space cotroller desig or liear systems = A + Bu y = C+, ( give T ( = AT A( T τ + ( τ τ = T e e Bu dτ Goal: id a liear cotrol law u=k such that the closed loop system = A + BK = ( A + BK is stable at e =. Remarks Stability determied by eigevalues use K to make eigevalues o (A+BK stable Ca also lik eigevalues to perormace (eg, iitial coditio respose Questio: whe ca we place the eigevalues ayplace that we wat? Thm The eigevalues o (A+BK ca be set to arbitrary values i ad oly i the pair (A,B is cotrollable. MATLAB: K = place(a, B, eigs 8 Oct CDS /8/ 5

6 CDS, Lecture 5. Natural dyamics = b a b r = a d b Eample #: Predator prey Cotrolled dyamics: modulate ood supply = b ( + u a b r = a d b 8 6 Q: ca we move rom some iitial populatio o oes ad rabbits to a speciied oe i time T by modulatio i the ood supply? Q: ca we stabilize the populatio aroud the curret equilibrum poit Approach: try to aswer this questio locally, aroud the equilibrium poit Oct CDS Eample #: Problem setup Equilibrium poit calculatio = br ( + u a b = a d b e = ( Liearizatio Compute liearizatio aroud equil. poit, e : A= B = u ( e, ue ( e, ue Redeie local variables: z=- e, v=u-u e % Compute the equil poit % predprey.m cotais dyamics = ilie('predprey(,'; eq = solve(, [5,5]; % Compute liearizatio A = [ br - a*eq( - *b*eq(, -a*eq(; a*eq(, -d + a*eq( - *b*eq( ]; B = [br*eq(; ]; d z br a, e b, e a, e z b r, e v dt z = a, e d a, e b, e z + + Cotrollable? YES, i b r,a (check [B AB] ca locally steer to ay poit 8 Oct CDS /8/ 6

7 CDS, Lecture 5. Eample #: Stabilizatio via eigevalue assigmet d z br a, e b, e a, e z b r, e v dt z = a, e d a, e b +, e z + Cotrol desig: v = Kz = K( e u = u + v Place poles at stable values Choose λ=-, - K = place(a, B, [-; -]; e Modiy dyamics to iclude cotrol = b ( K( a b a d b r e = Oct CDS 3 How to Assig Eigevalues Eigevalue locatio determies time respose For each eigevalue λ i =σ i + jω i, get cotributio o the orm ( ω ω σt y ( t = e asi( t + bcos( t i Imag Ais - Pole-zero map ω p Amplitude To: Y( Step Respose From: U( T π/ω p Repeated eigevalues ca give additioal terms o the orm t k e σ +jω Real Ais Time (sec. Eigevalue assigemet chages the dyamics o the system Illustrates oe o the mai priciples o eedback Ca be used to make ustable poits stable, icrease the speed o the respose, etc Cautio: eigevalue assigmet aects magitude o iput required The amout o actuator eort required to chage the dyamics rom the atural (ope loop dyamics to the desired (closed loop dyamics ca be large We will lear more sophisticated ways to deal with this tradeo later i the course 8 Oct CDS 4 /8/ 7

8 CDS, Lecture 5. State Space Feedback Desig Tools Eigevalue assigmet (also called pole placemet Choose eigevalue locatios to correspod to desired dyamics Optimal cotrol (CDS b Choose the eedback that miimizes a cost uctio: ( T T J = Q+ urudt u = K K = lqr(a, B, Q, R Key advatage is the eplicit tradeo betwee state error ad iput magitude Ote very heard to relate the weights (Q, R to the desired system behavior Noliear systems (CDS I liearizatio is cotrollable, ca show that liear eedback provides local stability More geerally, ca look at oliear state eedback, u = α( Optimal cotrol problem ca t be solved i closed orm Alterative: Lyapuov based desig ( itegrator backsteppig, Lypuov redesig, etc 8 Oct CDS 5 Summary: Cotrollability ad State Space Feedback = A + Bu y = C+ B AB A B A B u = u + K( e e Key cocepts Cotrollability: id u s.t. Cotrollability rak test or liear systems State eedback to assig eigevalues Oct CDS 6 /8/ 8

CDS 101: Lecture 5.1 Reachability and State Space Feedback

CDS 101: Lecture 5.1 Reachability and State Space Feedback CDS, Lctur 5. CDS : Lctur 5. Rachability ad Stat Spac Fdback Richard M. Murray 7 Octobr 3 Goals: Di rachability o a cotrol systm Giv tsts or rachability o liar systms ad apply to ampls Dscrib th dsig o

More information

CDS 101: Lecture 5.1 Reachability and State Space Feedback

CDS 101: Lecture 5.1 Reachability and State Space Feedback CDS, Lctur 5. CDS : Lctur 5. Rachability ad Stat Spac Fdback Richard M. Murray ad Hido Mabuchi 5 Octobr 4 Goals: Di rachability o a cotrol systm Giv tsts or rachability o liar systms ad apply to ampls

More information

CDS 101: Lecture 5-1 Reachability and State Space Feedback

CDS 101: Lecture 5-1 Reachability and State Space Feedback CDS 11: Lecture 5-1 Reachability and State Space Feedback Richard M. Murray 23 October 26 Goals: Define reachability of a control system Give tests for reachability of linear systems and apply to examples

More information

CDS 101: Lecture 8.2 Tools for PID & Loop Shaping

CDS 101: Lecture 8.2 Tools for PID & Loop Shaping CDS : Lecture 8. Tools for PID & Loop Shapig Richard M. Murray 7 November 4 Goals: Show how to use loop shapig to achieve a performace specificatio Itroduce ew tools for loop shapig desig: Ziegler-Nichols,

More information

CS537. Numerical Analysis and Computing

CS537. Numerical Analysis and Computing CS57 Numerical Aalysis ad Computig Lecture Locatig Roots o Equatios Proessor Ju Zhag Departmet o Computer Sciece Uiversity o Ketucky Leigto KY 456-6 Jauary 9 9 What is the Root May physical system ca be

More information

CS321. Numerical Analysis and Computing

CS321. Numerical Analysis and Computing CS Numerical Aalysis ad Computig Lecture Locatig Roots o Equatios Proessor Ju Zhag Departmet o Computer Sciece Uiversity o Ketucky Leigto KY 456-6 September 8 5 What is the Root May physical system ca

More information

Castiel, Supernatural, Season 6, Episode 18

Castiel, Supernatural, Season 6, Episode 18 13 Differetial Equatios the aswer to your questio ca best be epressed as a series of partial differetial equatios... Castiel, Superatural, Seaso 6, Episode 18 A differetial equatio is a mathematical equatio

More information

Notes on iteration and Newton s method. Iteration

Notes on iteration and Newton s method. Iteration Notes o iteratio ad Newto s method Iteratio Iteratio meas doig somethig over ad over. I our cotet, a iteratio is a sequece of umbers, vectors, fuctios, etc. geerated by a iteratio rule of the type 1 f

More information

CDS 101: Lecture 4.1 Linear Systems

CDS 101: Lecture 4.1 Linear Systems CDS : Lecture 4. Linear Systems Richard M. Murray 8 October 4 Goals: Describe linear system models: properties, eamples, and tools Characterize stability and performance of linear systems in terms of eigenvalues

More information

Lyapunov Stability Analysis for Feedback Control Design

Lyapunov Stability Analysis for Feedback Control Design Copyright F.L. Lewis 008 All rights reserved Updated: uesday, November, 008 Lyapuov Stability Aalysis for Feedbac Cotrol Desig Lyapuov heorems Lyapuov Aalysis allows oe to aalyze the stability of cotiuous-time

More information

THE SOLUTION OF NONLINEAR EQUATIONS f( x ) = 0.

THE SOLUTION OF NONLINEAR EQUATIONS f( x ) = 0. THE SOLUTION OF NONLINEAR EQUATIONS f( ) = 0. Noliear Equatio Solvers Bracketig. Graphical. Aalytical Ope Methods Bisectio False Positio (Regula-Falsi) Fied poit iteratio Newto Raphso Secat The root of

More information

Introduction to Signals and Systems, Part V: Lecture Summary

Introduction to Signals and Systems, Part V: Lecture Summary EEL33: Discrete-Time Sigals ad Systems Itroductio to Sigals ad Systems, Part V: Lecture Summary Itroductio to Sigals ad Systems, Part V: Lecture Summary So far we have oly looked at examples of o-recursive

More information

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j. Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α

More information

Problem 1. Problem Engineering Dynamics Problem Set 9--Solution. Find the equation of motion for the system shown with respect to:

Problem 1. Problem Engineering Dynamics Problem Set 9--Solution. Find the equation of motion for the system shown with respect to: 2.003 Egieerig Dyamics Problem Set 9--Solutio Problem 1 Fid the equatio of motio for the system show with respect to: a) Zero sprig force positio. Draw the appropriate free body diagram. b) Static equilibrium

More information

Course Outline. Designing Control Systems. Proportional Controller. Amme 3500 : System Dynamics and Control. Root Locus. Dr. Stefan B.

Course Outline. Designing Control Systems. Proportional Controller. Amme 3500 : System Dynamics and Control. Root Locus. Dr. Stefan B. Amme 3500 : System Dyamics ad Cotrol Root Locus Course Outlie Week Date Cotet Assigmet Notes Mar Itroductio 8 Mar Frequecy Domai Modellig 3 5 Mar Trasiet Performace ad the s-plae 4 Mar Block Diagrams Assig

More information

State Space Representation

State Space Representation Optimal Cotrol, Guidace ad Estimatio Lecture 2 Overview of SS Approach ad Matrix heory Prof. Radhakat Padhi Dept. of Aerospace Egieerig Idia Istitute of Sciece - Bagalore State Space Represetatio Prof.

More information

Time-Domain Representations of LTI Systems

Time-Domain Representations of LTI Systems 2.1 Itroductio Objectives: 1. Impulse resposes of LTI systems 2. Liear costat-coefficiets differetial or differece equatios of LTI systems 3. Bloc diagram represetatios of LTI systems 4. State-variable

More information

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,

More information

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows

More information

EE Control Systems

EE Control Systems Copyright FL Lewis 7 All rights reserved Updated: Moday, November 1, 7 EE 4314 - Cotrol Systems Bode Plot Performace Specificatios The Bode Plot was developed by Hedrik Wade Bode i 1938 while he worked

More information

Chapter 2 Feedback Control Theory Continued

Chapter 2 Feedback Control Theory Continued Chapter Feedback Cotrol Theor Cotiued. Itroductio I the previous chapter, the respose characteristic of simple first ad secod order trasfer fuctios were studied. It was show that first order trasfer fuctio,

More information

TAYLOR AND MACLAURIN SERIES

TAYLOR AND MACLAURIN SERIES Calculus TAYLOR AND MACLAURIN SERIES Give a uctio ( ad a poit a, we wish to approimate ( i the eighborhood o a by a polyomial o degree. c c ( a c( a c( a P ( c ( a We have coeiciets to choose. We require

More information

(c) Write, but do not evaluate, an integral expression for the volume of the solid generated when R is

(c) Write, but do not evaluate, an integral expression for the volume of the solid generated when R is Calculus BC Fial Review Name: Revised 7 EXAM Date: Tuesday, May 9 Remiders:. Put ew batteries i your calculator. Make sure your calculator is i RADIAN mode.. Get a good ight s sleep. Eat breakfast. Brig:

More information

Answer: 1(A); 2(C); 3(A); 4(D); 5(B); 6(A); 7(C); 8(C); 9(A); 10(A); 11(A); 12(C); 13(C)

Answer: 1(A); 2(C); 3(A); 4(D); 5(B); 6(A); 7(C); 8(C); 9(A); 10(A); 11(A); 12(C); 13(C) Aswer: (A); (C); 3(A); 4(D); 5(B); 6(A); 7(C); 8(C); 9(A); 0(A); (A); (C); 3(C). A two loop positio cotrol system is show below R(s) Y(s) + + s(s +) - - s The gai of the Tacho-geerator iflueces maily the

More information

Chapter 4 : Laplace Transform

Chapter 4 : Laplace Transform 4. Itroductio Laplace trasform is a alterative to solve the differetial equatio by the complex frequecy domai ( s = σ + jω), istead of the usual time domai. The DE ca be easily trasformed ito a algebraic

More information

1. Linearization of a nonlinear system given in the form of a system of ordinary differential equations

1. Linearization of a nonlinear system given in the form of a system of ordinary differential equations . Liearizatio of a oliear system give i the form of a system of ordiary differetial equatios We ow show how to determie a liear model which approximates the behavior of a time-ivariat oliear system i a

More information

Taylor Polynomials and Approximations - Classwork

Taylor Polynomials and Approximations - Classwork Taylor Polyomials ad Approimatios - Classwork Suppose you were asked to id si 37 o. You have o calculator other tha oe that ca do simple additio, subtractio, multiplicatio, or divisio. Fareched\ Not really.

More information

Appendix: The Laplace Transform

Appendix: The Laplace Transform Appedix: The Laplace Trasform The Laplace trasform is a powerful method that ca be used to solve differetial equatio, ad other mathematical problems. Its stregth lies i the fact that it allows the trasformatio

More information

Math 312 Lecture Notes One Dimensional Maps

Math 312 Lecture Notes One Dimensional Maps Math 312 Lecture Notes Oe Dimesioal Maps Warre Weckesser Departmet of Mathematics Colgate Uiversity 21-23 February 25 A Example We begi with the simplest model of populatio growth. Suppose, for example,

More information

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A)

REGRESSION (Physics 1210 Notes, Partial Modified Appendix A) REGRESSION (Physics 0 Notes, Partial Modified Appedix A) HOW TO PERFORM A LINEAR REGRESSION Cosider the followig data poits ad their graph (Table I ad Figure ): X Y 0 3 5 3 7 4 9 5 Table : Example Data

More information

Chapter 7: The z-transform. Chih-Wei Liu

Chapter 7: The z-transform. Chih-Wei Liu Chapter 7: The -Trasform Chih-Wei Liu Outlie Itroductio The -Trasform Properties of the Regio of Covergece Properties of the -Trasform Iversio of the -Trasform The Trasfer Fuctio Causality ad Stability

More information

Introduction to Algorithms

Introduction to Algorithms Itroductio to Algorithms 6.046J/8.40J LECTURE 9 Radomly built biary search trees Epected ode depth Aalyzig height Coveity lemma Jese s iequality Epoetial height Post mortem Pro. Eri Demaie October 7, 2005

More information

Signals and Systems. Problem Set: From Continuous-Time to Discrete-Time

Signals and Systems. Problem Set: From Continuous-Time to Discrete-Time Sigals ad Systems Problem Set: From Cotiuous-Time to Discrete-Time Updated: October 5, 2017 Problem Set Problem 1 - Liearity ad Time-Ivariace Cosider the followig systems ad determie whether liearity ad

More information

Continuous Models for Eigenvalue Problems

Continuous Models for Eigenvalue Problems Cotiuous Models for Eigevalue Problems Li-Zhi Liao (http://www.math.hkbu.edu.hk/~liliao) Departmet of Mathematics Hog Kog Baptist Uiversity Dedicate this talk to the memory of Gee Golub Program for Gee

More information

Chapter 4. Fourier Series

Chapter 4. Fourier Series Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,

More information

Dynamic Response of Linear Systems

Dynamic Response of Linear Systems Dyamic Respose of Liear Systems Liear System Respose Superpositio Priciple Resposes to Specific Iputs Dyamic Respose of st Order Systems Characteristic Equatio - Free Respose Stable st Order System Respose

More information

Representing Functions as Power Series. 3 n ...

Representing Functions as Power Series. 3 n ... Math Fall 7 Lab Represetig Fuctios as Power Series I. Itrouctio I sectio.8 we leare the series c c c c c... () is calle a power series. It is a uctio o whose omai is the set o all or which it coverges.

More information

Algebra II Notes Unit Seven: Powers, Roots, and Radicals

Algebra II Notes Unit Seven: Powers, Roots, and Radicals Syllabus Objectives: 7. The studets will use properties of ratioal epoets to simplify ad evaluate epressios. 7.8 The studet will solve equatios cotaiig radicals or ratioal epoets. b a, the b is the radical.

More information

( ) (( ) ) ANSWERS TO EXERCISES IN APPENDIX B. Section B.1 VECTORS AND SETS. Exercise B.1-1: Convex sets. are convex, , hence. and. (a) Let.

( ) (( ) ) ANSWERS TO EXERCISES IN APPENDIX B. Section B.1 VECTORS AND SETS. Exercise B.1-1: Convex sets. are convex, , hence. and. (a) Let. Joh Riley 8 Jue 03 ANSWERS TO EXERCISES IN APPENDIX B Sectio B VECTORS AND SETS Exercise B-: Covex sets (a) Let 0 x, x X, X, hece 0 x, x X ad 0 x, x X Sice X ad X are covex, x X ad x X The x X X, which

More information

Introduction to Algorithms

Introduction to Algorithms Itroductio to Algorithms 6.046J/8.40J/SMA5503 Lecture 9 Pro. Charles E. Leiserso Biary-search-tree sort T Create a empty BST or i to do TREE-INSERT(T, A[i]) Perorm a iorder tree wal o T. Eample: A [3 8

More information

Crash course part 2. Frequency compensation

Crash course part 2. Frequency compensation Crash course part Frequecy compesatio Ageda Frequecy depedace Feedback amplifiers Frequecy depedace of the Trasistor Frequecy Compesatio Phatom Zero Examples Crash course part poles ad zeros I geeral a

More information

You may work in pairs or purely individually for this assignment.

You may work in pairs or purely individually for this assignment. CS 04 Problem Solvig i Computer Sciece OOC Assigmet 6: Recurreces You may work i pairs or purely idividually for this assigmet. Prepare your aswers to the followig questios i a plai ASCII text file or

More information

Discrete-Time Signals and Systems. Discrete-Time Signals and Systems. Signal Symmetry. Elementary Discrete-Time Signals.

Discrete-Time Signals and Systems. Discrete-Time Signals and Systems. Signal Symmetry. Elementary Discrete-Time Signals. Discrete-ime Sigals ad Systems Discrete-ime Sigals ad Systems Dr. Deepa Kudur Uiversity of oroto Referece: Sectios. -.5 of Joh G. Proakis ad Dimitris G. Maolakis, Digital Sigal Processig: Priciples, Algorithms,

More information

Lecture 7: Polar representation of complex numbers

Lecture 7: Polar representation of complex numbers Lecture 7: Polar represetatio of comple umbers See FLAP Module M3.1 Sectio.7 ad M3. Sectios 1 ad. 7.1 The Argad diagram I two dimesioal Cartesia coordiates (,), we are used to plottig the fuctio ( ) with

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

ADVANCED DIGITAL SIGNAL PROCESSING

ADVANCED DIGITAL SIGNAL PROCESSING ADVANCED DIGITAL SIGNAL PROCESSING PROF. S. C. CHAN (email : sccha@eee.hku.hk, Rm. CYC-702) DISCRETE-TIME SIGNALS AND SYSTEMS MULTI-DIMENSIONAL SIGNALS AND SYSTEMS RANDOM PROCESSES AND APPLICATIONS ADAPTIVE

More information

( ) = p and P( i = b) = q.

( ) = p and P( i = b) = q. MATH 540 Radom Walks Part 1 A radom walk X is special stochastic process that measures the height (or value) of a particle that radomly moves upward or dowward certai fixed amouts o each uit icremet of

More information

Discrete-Time System Properties. Discrete-Time System Properties. Terminology: Implication. Terminology: Equivalence. Reference: Section 2.

Discrete-Time System Properties. Discrete-Time System Properties. Terminology: Implication. Terminology: Equivalence. Reference: Section 2. Professor Deepa Kudur Uiversity of oroto Referece: Sectio 2.2 Joh G. Proakis ad Dimitris G. Maolakis, Digital Sigal Processig: Priciples, Algorithms, ad Applicatios, 4th editio, 2007. Professor Deepa Kudur

More information

AN OPEN-PLUS-CLOSED-LOOP APPROACH TO SYNCHRONIZATION OF CHAOTIC AND HYPERCHAOTIC MAPS

AN OPEN-PLUS-CLOSED-LOOP APPROACH TO SYNCHRONIZATION OF CHAOTIC AND HYPERCHAOTIC MAPS http://www.paper.edu.c Iteratioal Joural of Bifurcatio ad Chaos, Vol. 1, No. 5 () 119 15 c World Scietific Publishig Compay AN OPEN-PLUS-CLOSED-LOOP APPROACH TO SYNCHRONIZATION OF CHAOTIC AND HYPERCHAOTIC

More information

Maximum and Minimum Values

Maximum and Minimum Values Sec 4.1 Maimum ad Miimum Values A. Absolute Maimum or Miimum / Etreme Values A fuctio Similarly, f has a Absolute Maimum at c if c f f has a Absolute Miimum at c if c f f for every poit i the domai. f

More information

Applications in Linear Algebra and Uses of Technology

Applications in Linear Algebra and Uses of Technology 1 TI-89: Let A 1 4 5 6 7 8 10 Applicatios i Liear Algebra ad Uses of Techology,adB 4 1 1 4 type i: [1,,;4,5,6;7,8,10] press: STO type i: A type i: [4,-1;-1,4] press: STO (1) Row Echelo Form: MATH/matrix

More information

Example 2. Find the upper bound for the remainder for the approximation from Example 1.

Example 2. Find the upper bound for the remainder for the approximation from Example 1. Lesso 8- Error Approimatios 0 Alteratig Series Remaider: For a coverget alteratig series whe approimatig the sum of a series by usig oly the first terms, the error will be less tha or equal to the absolute

More information

LINEARIZATION OF NONLINEAR EQUATIONS By Dominick Andrisani. dg x. ( ) ( ) dx

LINEARIZATION OF NONLINEAR EQUATIONS By Dominick Andrisani. dg x. ( ) ( ) dx LINEAIZATION OF NONLINEA EQUATIONS By Domiick Adrisai A. Liearizatio of Noliear Fctios A. Scalar fctios of oe variable. We are ive the oliear fctio (). We assme that () ca be represeted si a Taylor series

More information

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 4 Solutions [Numerical Methods]

ENGI 4430 Advanced Calculus for Engineering Faculty of Engineering and Applied Science Problem Set 4 Solutions [Numerical Methods] ENGI 3 Advaced Calculus or Egieerig Facult o Egieerig ad Applied Sciece Problem Set Solutios [Numerical Methods]. Use Simpso s rule with our itervals to estimate I si d a, b, h a si si.889 si 3 si.889

More information

2.004 Dynamics and Control II Spring 2008

2.004 Dynamics and Control II Spring 2008 MIT OpeCourseWare http://ocw.mit.edu 2.004 Dyamics ad Cotrol II Sprig 2008 For iformatio about citig these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Massachusetts Istitute of Techology

More information

State space systems analysis

State space systems analysis State pace ytem aalyi Repreetatio of a ytem i tate-pace (tate-pace model of a ytem To itroduce the tate pace formalim let u tart with a eample i which the ytem i dicuio i a imple electrical circuit with

More information

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5 Sigals ad Systems Sigals ad Systems Sigals are variables that carry iformatio Systemstake sigals as iputs ad produce sigals as outputs The course deals with the passage of sigals through systems T-6.4

More information

Exponential and Trigonometric Functions Lesson #1

Exponential and Trigonometric Functions Lesson #1 Epoetial ad Trigoometric Fuctios Lesso # Itroductio To Epoetial Fuctios Cosider a populatio of 00 mice which is growig uder plague coditios. If the mouse populatio doubles each week we ca costruct a table

More information

Most text will write ordinary derivatives using either Leibniz notation 2 3. y + 5y= e and y y. xx tt t

Most text will write ordinary derivatives using either Leibniz notation 2 3. y + 5y= e and y y. xx tt t Itroductio to Differetial Equatios Defiitios ad Termiolog Differetial Equatio: A equatio cotaiig the derivatives of oe or more depedet variables, with respect to oe or more idepedet variables, is said

More information

Mon Apr Second derivative test, and maybe another conic diagonalization example. Announcements: Warm-up Exercise:

Mon Apr Second derivative test, and maybe another conic diagonalization example. Announcements: Warm-up Exercise: Math 2270-004 Week 15 otes We will ot ecessarily iish the material rom a give day's otes o that day We may also add or subtract some material as the week progresses, but these otes represet a i-depth outlie

More information

DIGITAL SIGNAL PROCESSING LECTURE 3

DIGITAL SIGNAL PROCESSING LECTURE 3 DIGITAL SIGNAL PROCESSING LECTURE 3 Fall 2 2K8-5 th Semester Tahir Muhammad tmuhammad_7@yahoo.com Cotet ad Figures are from Discrete-Time Sigal Processig, 2e by Oppeheim, Shafer, ad Buc, 999-2 Pretice

More information

September 2012 C1 Note. C1 Notes (Edexcel) Copyright - For AS, A2 notes and IGCSE / GCSE worksheets 1

September 2012 C1 Note. C1 Notes (Edexcel) Copyright   - For AS, A2 notes and IGCSE / GCSE worksheets 1 September 0 s (Edecel) Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright

More information

Lab(8) controller design using root locus

Lab(8) controller design using root locus Lab(8) cotroller desig usig root locus I this lab we will lear how to desig a cotroller usig root locus but before this we eed to aswer the followig questios: What is root locus? What is the purpose of

More information

CALIFORNIA INSTITUTE OF TECHNOLOGY. Control and Dynamical Systems CDS 270. Eugene Lavretsky Spring 2007

CALIFORNIA INSTITUTE OF TECHNOLOGY. Control and Dynamical Systems CDS 270. Eugene Lavretsky Spring 2007 CALIFORNIA INSTITUTE OF TECHNOLOGY Cotrol ad Dyamical Systems CDS 7 Eugee Lavretsky Sprig 7 Lecture 1 1. Itroductio Readig material: [1]: Chapter 1, Sectios 1.1, 1. [1]: Chapter 3, Sectio 3.1 []: Chapter

More information

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution EEL5: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we begi our mathematical treatmet of discrete-time s. As show i Figure, a discrete-time operates or trasforms some iput sequece x [

More information

Lesson 10: Limits and Continuity

Lesson 10: Limits and Continuity www.scimsacademy.com Lesso 10: Limits ad Cotiuity SCIMS Academy 1 Limit of a fuctio The cocept of limit of a fuctio is cetral to all other cocepts i calculus (like cotiuity, derivative, defiite itegrals

More information

Some Variants of Newton's Method with Fifth-Order and Fourth-Order Convergence for Solving Nonlinear Equations

Some Variants of Newton's Method with Fifth-Order and Fourth-Order Convergence for Solving Nonlinear Equations Copyright, Darbose Iteratioal Joural o Applied Mathematics ad Computatio Volume (), pp -6, 9 http//: ijamc.darbose.com Some Variats o Newto's Method with Fith-Order ad Fourth-Order Covergece or Solvig

More information

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION - Vol. XII - Feedback Linearization of Nonlinear Systems - Alberto Isidori and Claudio De Persis

CONTROL SYSTEMS, ROBOTICS AND AUTOMATION - Vol. XII - Feedback Linearization of Nonlinear Systems - Alberto Isidori and Claudio De Persis FEEDBACK LINEARIZATION OF NONLINEAR SYSTEMS Alberto Isidori Dipartimeto di Iformatica e Sistemistica, Uiversità di Roma La Sapieza" ad Departmet of Systems Sciece ad Mathematics, Washigto Uiversity i St.

More information

Statistical and Mathematical Methods DS-GA 1002 December 8, Sample Final Problems Solutions

Statistical and Mathematical Methods DS-GA 1002 December 8, Sample Final Problems Solutions Statistical ad Mathematical Methods DS-GA 00 December 8, 05. Short questios Sample Fial Problems Solutios a. Ax b has a solutio if b is i the rage of A. The dimesio of the rage of A is because A has liearly-idepedet

More information

Transfer Function Analysis

Transfer Function Analysis Trasfer Fuctio Aalysis Free & Forced Resposes Trasfer Fuctio Syste Stability ME375 Trasfer Fuctios - Free & Forced Resposes Ex: Let s s look at a stable first order syste: τ y + y = Ku Take LT of the I/O

More information

Chapter 6 Principles of Data Reduction

Chapter 6 Principles of Data Reduction Chapter 6 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 0 Chapter 6 Priciples of Data Reductio Sectio 6. Itroductio Goal: To summarize or reduce the data X, X,, X to get iformatio about a

More information

Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore

Dr. Radhakant Padhi Asst. Professor Dept. of Aerospace Engineering Indian Institute of Science - Bangalore Lecture 0 Coversio Betwee State Space ad Trasfer Fuctio Represetatios i Liear Systems II Dr. Radhakat Padhi Asst. Professor Dept. of Aerospace Egieerig Idia Istitute of Sciece - Bagalore A Alterate First

More information

8. Applications To Linear Differential Equations

8. Applications To Linear Differential Equations 8. Applicatios To Liear Differetial Equatios 8.. Itroductio 8.. Review Of Results Cocerig Liear Differetial Equatios Of First Ad Secod Orders 8.3. Eercises 8.4. Liear Differetial Equatios Of Order N 8.5.

More information

Taylor Series (BC Only)

Taylor Series (BC Only) Studet Study Sessio Taylor Series (BC Oly) Taylor series provide a way to fid a polyomial look-alike to a o-polyomial fuctio. This is doe by a specific formula show below (which should be memorized): Taylor

More information

Lecture 13. Graphical representation of the frequency response. Luca Ferrarini - Basic Automatic Control 1

Lecture 13. Graphical representation of the frequency response. Luca Ferrarini - Basic Automatic Control 1 Lecture 3 Graphical represetatio of the frequecy respose Luca Ferrarii - Basic Automatic Cotrol Graphical represetatio of the frequecy respose Polar plot G Bode plot ( j), G Im 3 Re of the magitude G (

More information

Math E-21b Spring 2018 Homework #2

Math E-21b Spring 2018 Homework #2 Math E- Sprig 08 Homework # Prolems due Thursday, Feruary 8: Sectio : y = + 7 8 Fid the iverse of the liear trasformatio [That is, solve for, i terms of y, y ] y = + 0 Cosider the circular face i the accompayig

More information

Student s Printed Name:

Student s Printed Name: Studet s Prited Name: Istructor: XID: C Sectio: No questios will be aswered durig this eam. If you cosider a questio to be ambiguous, state your assumptios i the margi ad do the best you ca to provide

More information

Mon Feb matrix inverses. Announcements: Warm-up Exercise:

Mon Feb matrix inverses. Announcements: Warm-up Exercise: Math 225-4 Week 6 otes We will ot ecessarily fiish the material from a give day's otes o that day We may also add or subtract some material as the week progresses, but these otes represet a i-depth outlie

More information

Chapter 7 z-transform

Chapter 7 z-transform Chapter 7 -Trasform Itroductio Trasform Uilateral Trasform Properties Uilateral Trasform Iversio of Uilateral Trasform Determiig the Frequecy Respose from Poles ad Zeros Itroductio Role i Discrete-Time

More information

Calculus 2 - D. Yuen Final Exam Review (Version 11/22/2017. Please report any possible typos.)

Calculus 2 - D. Yuen Final Exam Review (Version 11/22/2017. Please report any possible typos.) Calculus - D Yue Fial Eam Review (Versio //7 Please report ay possible typos) NOTE: The review otes are oly o topics ot covered o previous eams See previous review sheets for summary of previous topics

More information

1.225J J (ESD 205) Transportation Flow Systems

1.225J J (ESD 205) Transportation Flow Systems .5J J (ESD 5 Trasportatio Flow Systems Lecture 6 Itroductio to Optimizatio Pro. Ismail Chabii ad Pro. Amedeo R. Odoi Lecture 6 Outlie Mathematical programs (MPs Formulatio o shortest path problems as MPs

More information

mx bx kx F t. dt IR I LI V t, Q LQ RQ V t,

mx bx kx F t. dt IR I LI V t, Q LQ RQ V t, Lecture 5 omplex Variables II (Applicatios i Physics) (See hapter i Boas) To see why complex variables are so useful cosider first the (liear) mechaics of a sigle particle described by Newto s equatio

More information

MATH 372: Difference Equations

MATH 372: Difference Equations MATH 372: Differece Equatios G. de Vries February 23, 2003 1 Itroductio I this chapter, we use discrete models to describe dyamical pheomea i biology. Discrete models are appropriate whe oe ca thik about

More information

EE 4343 Lab#4 PID Control Design of Rigid Bodies

EE 4343 Lab#4 PID Control Design of Rigid Bodies EE 44 Lab#4 PID Cotrol Desig of Rigid Bodies Prepared by: Stacy Caso E-mail: scaso@arri.uta.edu Updated: July 9, 1999 This lab demostrates some key cocepts associated with proportioal plus derivative (PD

More information

FIR Filter Design: Part I

FIR Filter Design: Part I EEL3: Discrete-Time Sigals ad Systems FIR Filter Desig: Part I. Itroductio FIR Filter Desig: Part I I this set o otes, we cotiue our exploratio o the requecy respose o FIR ilters. First, we cosider some

More information

Lecture 11. Solution of Nonlinear Equations - III

Lecture 11. Solution of Nonlinear Equations - III Eiciecy o a ethod Lecture Solutio o Noliear Equatios - III The eiciecy ide o a iterative ethod is deied by / E r r: rate o covergece o the ethod : total uber o uctios ad derivative evaluatios at each step

More information

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian?

NBHM QUESTION 2007 Section 1 : Algebra Q1. Let G be a group of order n. Which of the following conditions imply that G is abelian? NBHM QUESTION 7 NBHM QUESTION 7 NBHM QUESTION 7 Sectio : Algebra Q Let G be a group of order Which of the followig coditios imply that G is abelia? 5 36 Q Which of the followig subgroups are ecesarily

More information

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform Sigal Processig i Mechatroics Summer semester, 1 Lecture 3, Covolutio, Fourier Series ad Fourier rasform Dr. Zhu K.P. AIS, UM 1 1. Covolutio Covolutio Descriptio of LI Systems he mai premise is that the

More information

Bayesian Methods: Introduction to Multi-parameter Models

Bayesian Methods: Introduction to Multi-parameter Models Bayesia Methods: Itroductio to Multi-parameter Models Parameter: θ = ( θ, θ) Give Likelihood p(y θ) ad prior p(θ ), the posterior p proportioal to p(y θ) x p(θ ) Margial posterior ( θ, θ y) is Iterested

More information

ME 539, Fall 2008: Learning-Based Control

ME 539, Fall 2008: Learning-Based Control ME 539, Fall 2008: Learig-Based Cotrol Neural Network Basics 10/1/2008 & 10/6/2008 Uiversity Orego State Neural Network Basics Questios??? Aoucemet: Homework 1 has bee posted Due Friday 10/10/08 at oo

More information

µ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion

µ and π p i.e. Point Estimation x And, more generally, the population proportion is approximately equal to a sample proportion Poit Estimatio Poit estimatio is the rather simplistic (ad obvious) process of usig the kow value of a sample statistic as a approximatio to the ukow value of a populatio parameter. So we could for example

More information

Solution of EECS 315 Final Examination F09

Solution of EECS 315 Final Examination F09 Solutio of EECS 315 Fial Examiatio F9 1. Fid the umerical value of δ ( t + 4ramp( tdt. δ ( t + 4ramp( tdt. Fid the umerical sigal eergy of x E x = x[ ] = δ 3 = 11 = ( = ramp( ( 4 = ramp( 8 = 8 [ ] = (

More information

ECE 308 Discrete-Time Signals and Systems

ECE 308 Discrete-Time Signals and Systems ECE 38-5 ECE 38 Discrete-Time Sigals ad Systems Z. Aliyazicioglu Electrical ad Computer Egieerig Departmet Cal Poly Pomoa ECE 38-5 1 Additio, Multiplicatio, ad Scalig of Sequeces Amplitude Scalig: (A Costat

More information

Continuous Functions

Continuous Functions Cotiuous Fuctios Q What does it mea for a fuctio to be cotiuous at a poit? Aswer- I mathematics, we have a defiitio that cosists of three cocepts that are liked i a special way Cosider the followig defiitio

More information

Run-length & Entropy Coding. Redundancy Removal. Sampling. Quantization. Perform inverse operations at the receiver EEE

Run-length & Entropy Coding. Redundancy Removal. Sampling. Quantization. Perform inverse operations at the receiver EEE Geeral e Image Coder Structure Motio Video (s 1,s 2,t) or (s 1,s 2 ) Natural Image Samplig A form of data compressio; usually lossless, but ca be lossy Redudacy Removal Lossless compressio: predictive

More information

Precalculus MATH Sections 3.1, 3.2, 3.3. Exponential, Logistic and Logarithmic Functions

Precalculus MATH Sections 3.1, 3.2, 3.3. Exponential, Logistic and Logarithmic Functions Precalculus MATH 2412 Sectios 3.1, 3.2, 3.3 Epoetial, Logistic ad Logarithmic Fuctios Epoetial fuctios are used i umerous applicatios coverig may fields of study. They are probably the most importat group

More information

2C09 Design for seismic and climate changes

2C09 Design for seismic and climate changes 2C09 Desig for seismic ad climate chages Lecture 02: Dyamic respose of sigle-degree-of-freedom systems I Daiel Grecea, Politehica Uiversity of Timisoara 10/03/2014 Europea Erasmus Mudus Master Course Sustaiable

More information

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a Math E-2b Lecture #8 Notes This week is all about determiats. We ll discuss how to defie them, how to calculate them, lear the allimportat property kow as multiliearity, ad show that a square matrix A

More information

Abstract Vector Spaces. Abstract Vector Spaces

Abstract Vector Spaces. Abstract Vector Spaces Astract Vector Spaces The process of astractio is critical i egieerig! Physical Device Data Storage Vector Space MRI machie Optical receiver 0 0 1 0 1 0 0 1 Icreasig astractio 6.1 Astract Vector Spaces

More information

1 1 2 = show that: over variables x and y. [2 marks] Write down necessary conditions involving first and second-order partial derivatives for ( x0, y

1 1 2 = show that: over variables x and y. [2 marks] Write down necessary conditions involving first and second-order partial derivatives for ( x0, y Questio (a) A square matrix A= A is called positive defiite if the quadratic form waw > 0 for every o-zero vector w [Note: Here (.) deotes the traspose of a matrix or a vector]. Let 0 A = 0 = show that:

More information