Useful MATLAB Functions
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Input and Output Directions and Hankel Singular Values CEE 629
Principal Input and Output Directions and Hankel Singular Values CEE 629. System Identification Duke University, Fall 2017 1 Continuous-time systems in the frequency domain In the frequency domain, the input-output relationship of a LTI system (with r inputs, m outputs, and n internal states) is represented by the m-by-r rational frequency response function matrix equation y(ω) = H(ω)u(ω) . At a frequency ω a set of inputs with amplitudes u(ω) generate steady-state outputs with amplitudes y(ω). (These amplitude vectors are, in general, complex-valued, indicating mag- nitude and phase.) The singular value decomposition of the transfer function matrix is H(ω) = Y (ω) Σ(ω) U ∗(ω) (1) where: U(ω) is the r by r orthonormal matrix of input amplitude vectors, U ∗U = I, and Y (ω) is the m by m orthonormal matrix of output amplitude vectors, Y ∗Y = I Σ(ω) is the m by r diagonal matrix of singular values, Σ(ω) = diag(σ1(ω), σ2(ω), ··· σn(ω)) At any frequency ω, the singular values are ordered as: σ1(ω) ≥ σ2(ω) ≥ · · · ≥ σn(ω) ≥ 0 Re-arranging the singular value decomposition of H(s), H(ω)U(ω) = Y (ω) Σ(ω) or H(ω) ui(ω) = σi(ω) yi(ω) where ui(ω) and yi(ω) are the i-th columns of U(ω) and Y (ω). Since ||ui(ω)||2 = 1 and ||yi(ω)||2 = 1, the singular value σi(ω) represents the scaling from inputs with complex am- plitudes ui(ω) to outputs with amplitudes yi(ω). -
LABORATORY 3: Transient Circuits, RC, RL Step Responses, 2Nd Order Circuits
Alpha Laboratories ECSE-2010 LABORATORY 3: Transient circuits, RC, RL step responses, 2nd Order Circuits Note: If your partner is no longer in the class, please talk to the instructor. Material covered: RC circuits Integrators Differentiators 1st order RC, RL Circuits 2nd order RLC series, parallel circuits Thevenin circuits Part A: Transient Circuits RC Time constants: A time constant is the time it takes a circuit characteristic (Voltage for example) to change from one state to another state. In a simple RC circuit where the resistor and capacitor are in series, the RC time constant is defined as the time it takes the voltage across a capacitor to reach 63.2% of its final value when charging (or 36.8% of its initial value when discharging). It is assume a step function (Heavyside function) is applied as the source. The time constant is defined by the equation τ = RC where τ is the time constant in seconds R is the resistance in Ohms C is the capacitance in Farads The following figure illustrates the time constant for a square pulse when the capacitor is charging and discharging during the appropriate parts of the input signal. You will see a similar plot in the lab. Note the charge (63.2%) and discharge voltages (36.8%) after one time constant, respectively. Written by J. Braunstein Modified by S. Sawyer Spring 2020: 1/26/2020 Rensselaer Polytechnic Institute Troy, New York, USA 1 Alpha Laboratories ECSE-2010 Written by J. Braunstein Modified by S. Sawyer Spring 2020: 1/26/2020 Rensselaer Polytechnic Institute Troy, New York, USA 2 Alpha Laboratories ECSE-2010 Discovery Board: For most of the remaining class, you will want to compare input and output voltage time varying signals. -
Lecture 3: Transfer Function and Dynamic Response Analysis
Transfer function approach Dynamic response Summary Basics of Automation and Control I Lecture 3: Transfer function and dynamic response analysis Paweł Malczyk Division of Theory of Machines and Robots Institute of Aeronautics and Applied Mechanics Faculty of Power and Aeronautical Engineering Warsaw University of Technology October 17, 2019 © Paweł Malczyk. Basics of Automation and Control I Lecture 3: Transfer function and dynamic response analysis 1 / 31 Transfer function approach Dynamic response Summary Outline 1 Transfer function approach 2 Dynamic response 3 Summary © Paweł Malczyk. Basics of Automation and Control I Lecture 3: Transfer function and dynamic response analysis 2 / 31 Transfer function approach Dynamic response Summary Transfer function approach 1 Transfer function approach SISO system Definition Poles and zeros Transfer function for multivariable system Properties 2 Dynamic response 3 Summary © Paweł Malczyk. Basics of Automation and Control I Lecture 3: Transfer function and dynamic response analysis 3 / 31 Transfer function approach Dynamic response Summary SISO system Fig. 1: Block diagram of a single input single output (SISO) system Consider the continuous, linear time-invariant (LTI) system defined by linear constant coefficient ordinary differential equation (LCCODE): dny dn−1y + − + ··· + _ + = an n an 1 n−1 a1y a0y dt dt (1) dmu dm−1u = b + b − + ··· + b u_ + b u m dtm m 1 dtm−1 1 0 initial conditions y(0), y_(0),..., y(n−1)(0), and u(0),..., u(m−1)(0) given, u(t) – input signal, y(t) – output signal, ai – real constants for i = 1, ··· , n, and bj – real constants for j = 1, ··· , m. How do I find the LCCODE (1)? . -
An Approximate Transfer Function Model for a Double-Pipe Counter-Flow Heat Exchanger
energies Article An Approximate Transfer Function Model for a Double-Pipe Counter-Flow Heat Exchanger Krzysztof Bartecki Division of Control Science and Engineering, Opole University of Technology, ul. Prószkowska 76, 45-758 Opole, Poland; [email protected] Abstract: The transfer functions G(s) for different types of heat exchangers obtained from their par- tial differential equations usually contain some irrational components which reflect quite well their spatio-temporal dynamic properties. However, such a relatively complex mathematical representa- tion is often not suitable for various practical applications, and some kind of approximation of the original model would be more preferable. In this paper we discuss approximate rational transfer func- tions Gˆ(s) for a typical thick-walled double-pipe heat exchanger operating in the counter-flow mode. Using the semi-analytical method of lines, we transform the original partial differential equations into a set of ordinary differential equations representing N spatial sections of the exchanger, where each nth section can be described by a simple rational transfer function matrix Gn(s), n = 1, 2, ... , N. Their proper interconnection results in the overall approximation model expressed by a rational transfer function matrix Gˆ(s) of high order. As compared to the previously analyzed approximation model for the double-pipe parallel-flow heat exchanger which took the form of a simple, cascade interconnection of the sections, here we obtain a different connection structure which requires the use of the so-called linear fractional transformation with the Redheffer star product. Based on the resulting rational transfer function matrix Gˆ(s), the frequency and the steady-state responses of the approximate model are compared here with those obtained from the original irrational transfer Citation: Bartecki, K. -
A Passive Synthesis for Time-Invariant Transfer Functions
IEEE TRANSACTIONS ON CIRCUIT THEORY, VOL. CT-17, NO. 3, AUGUST 1970 333 A Passive Synthesis for Time-Invariant Transfer Functions PATRICK DEWILDE, STUDENT MEMBER, IEEE, LEONARD hiI. SILVERRJAN, MEMBER, IEEE, AND R. W. NEW-COMB, MEMBER, IEEE Absfroct-A passive transfer-function synthesis based upon state- [6], p. 307), in which a minimal-reactance scalar transfer- space techniques is presented. The method rests upon the formation function synthesis can be obtained; it provides a circuit of a coupling admittance that, when synthesized by. resistors and consideration of such concepts as stability, conkol- gyrators, is to be loaded by capacitors whose voltages form the state. By the use of a Lyapunov transformation, the coupling admittance lability, and observability. Background and the general is made positive real, while further transformations allow internal theory and position of state-space techniques in net- dissipation to be moved to the source or the load. A general class work theory can be found in [7]. of configurations applicable to integrated circuits and using only grounded gyrators, resistors, and a minimal number of capacitors II. PRELIMINARIES is obtained. The minimum number of resistors for the structure is also obtained. The technique illustrates how state-variable We first recall several facts pertinent to the intended theory can be used to obtain results not yet available through synthesis. other methods. Given a transfer function n X m matrix T(p) that is rational wit.h real coefficients (called real-rational) and I. INTRODUCTION that has T(m) = D, a finite constant matrix, there exist, real constant matrices A, B, C, such that (lk is the N ill, a procedure for time-v:$able minimum-re- k X lc identity, ,C[ ] is the Laplace transform) actance passive synthesis of a “stable” impulse response matrix was given based on a new-state- T(p) = D + C[pl, - A]-‘B, JXYI = T(~)=Wl equation technique for imbedding the impulse-response (14 matrix in a passive driving-point impulse-response matrix. -
Experiment 12: AC Circuits - RLC Circuit
Experiment 12: AC Circuits - RLC Circuit Introduction An inductor (L) is an important component of circuits, on the same level as resistors (R) and capacitors (C). The inductor is based on the principle of inductance - that moving charges create a magnetic field (the reverse is also true - a moving magnetic field creates an electric field). Inductors can be used to produce a desired magnetic field and store energy in its magnetic field, similar to capacitors being used to produce electric fields and storing energy in their electric field. At its simplest level, an inductor consists of a coil of wire in a circuit. The circuit symbol for an inductor is shown in Figure 1a. So far we observed that in an RC circuit the charge, current, and potential difference grew and decayed exponentially described by a time constant τ. If an inductor and a capacitor are connected in series in a circuit, the charge, current and potential difference do not grow/decay exponentially, but instead oscillate sinusoidally. In an ideal setting (no internal resistance) these oscillations will continue indefinitely with a period (T) and an angular frequency ! given by 1 ! = p (1) LC This is referred to as the circuit's natural angular frequency. A circuit containing a resistor, a capacitor, and an inductor is called an RLC circuit (or LCR), as shown in Figure 1b. With a resistor present, the total electromagnetic energy is no longer constant since energy is lost via Joule heating in the resistor. The oscillations of charge, current and potential are now continuously decreasing with amplitude. -
ELECTRICAL CIRCUIT ANALYSIS Lecture Notes
ELECTRICAL CIRCUIT ANALYSIS Lecture Notes (2020-21) Prepared By S.RAKESH Assistant Professor, Department of EEE Department of Electrical & Electronics Engineering Malla Reddy College of Engineering & Technology Maisammaguda, Dhullapally, Secunderabad-500100 B.Tech (EEE) R-18 MALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY II Year B.Tech EEE-I Sem L T/P/D C 3 -/-/- 3 (R18A0206) ELECTRICAL CIRCUIT ANALYSIS COURSE OBJECTIVES: This course introduces the analysis of transients in electrical systems, to understand three phase circuits, to evaluate network parameters of given electrical network, to draw the locus diagrams and to know about the networkfunctions To prepare the students to have a basic knowledge in the analysis of ElectricNetworks UNIT-I D.C TRANSIENT ANALYSIS: Transient response of R-L, R-C, R-L-C circuits (Series and parallel combinations) for D.C. excitations, Initial conditions, Solution using differential equation and Laplace transform method. UNIT - II A.C TRANSIENT ANALYSIS: Transient response of R-L, R-C, R-L-C Series circuits for sinusoidal excitations, Initial conditions, Solution using differential equation and Laplace transform method. UNIT - III THREE PHASE CIRCUITS: Phase sequence, Star and delta connection, Relation between line and phase voltages and currents in balanced systems, Analysis of balanced and Unbalanced three phase circuits UNIT – IV LOCUS DIAGRAMS & RESONANCE: Series and Parallel combination of R-L, R-C and R-L-C circuits with variation of various parameters.Resonance for series and parallel circuits, concept of band width and Q factor. UNIT - V NETWORK PARAMETERS:Two port network parameters – Z,Y, ABCD and hybrid parameters.Condition for reciprocity and symmetry.Conversion of one parameter to other, Interconnection of Two port networks in series, parallel and cascaded configuration and image parameters. -
Lecture 4: RLC Circuits and Resonant Circuits
Lecture 4: R-L-C Circuits and Resonant Circuits RLC series circuit: ● What's VR? ◆ Simplest way to solve for V is to use voltage divider equation in complex notation: V R X L X C V = in R R + XC + XL L C R Vin R = Vin = V0 cosω t 1 R + + jωL jωC ◆ Using complex notation for the apply voltage V = V cosωt = Real(V e jωt ): j t in 0 0 V e ω R V = 0 R $ 1 ' € R + j& ωL − ) % ωC ( ■ We are interested in the both the magnitude of VR and its phase with respect to Vin. ■ First the magnitude: jωt V0e R € V = R $ 1 ' R + j& ωL − ) % ωC ( V R = 0 2 2 $ 1 ' R +& ωL − ) % ωC ( K.K. Gan L4: RLC and Resonance Circuits 1 € ■ The phase of VR with respect to Vin can be found by writing VR in purely polar notation. ❑ For the denominator we have: 0 * 1 -4 2 ωL − $ 1 ' 2 $ 1 ' 2 1, /2 R + j& ωL − ) = R +& ωL − ) exp1 j tan− ωC 5 % C ( % C( , R / ω ω 2 , /2 3 + .6 ❑ Define the phase angle φ : Imaginary X tanφ = Real X € 1 ωL − = ωC R ❑ We can now write for VR in complex form: V R e jωt V = o R 2 € jφ 2 % 1 ( e R +' ωL − * Depending on L, C, and ω, the phase angle can be & ωC) positive or negative! In this example, if ωL > 1/ωC, j(ωt−φ) then VR(t) lags Vin(t). = VR e ■ Finally, we can write down the solution for V by taking the real part of the above equation: j(ωt−φ) V R e V R cos(ωt −φ) V = Real 0 = 0 R 2 2 € 2 % 1 ( 2 % 1 ( R +' ωL − * R +' ωL − * & ωC ) & ωC ) K.K. -
State Space Models
State Space Models MUS420 Equations of motion for any physical system may be Introduction to Linear State Space Models conveniently formulated in terms of its state x(t): Julius O. Smith III ([email protected]) Center for Computer Research in Music and Acoustics (CCRMA) Input Forces u(t) Department of Music, Stanford University Stanford, California 94305 ft Model State x(t) x˙(t) February 5, 2019 Outline R State Space Models x˙(t)= ft[x(t),u(t)] • where Linear State Space Formulation • x(t) = state of the system at time t Markov Parameters (Impulse Response) • u(t) = vector of external inputs (typically driving forces) Transfer Function • ft = general function mapping the current state x(t) and Difference Equations to State Space Models inputs u(t) to the state time-derivative x˙(t) • Similarity Transformations The function f may be time-varying, in general • • t Modal Representation (Diagonalization) This potentially nonlinear time-varying model is • • Matlab Examples extremely general (but causal) • Even the human brain can be modeled in this form • 1 2 State-Space History Key Property of State Vector The key property of the state vector x(t) in the state 1. Classic phase-space in physics (Gibbs 1901) space formulation is that it completely determines the System state = point in position-momentum space system at time t 2. Digital computer (1950s) 3. Finite State Machines (Mealy and Moore, 1960s) Future states depend only on the current state x(t) • and on any inputs u(t) at time t and beyond 4. Finite Automata All past states and the entire input history are 5. -
MUS420 Introduction to Linear State Space Models
State Space Models MUS420 Equations of motion for any physical system may be Introduction to Linear State Space Models conveniently formulated in terms of its state x(t): Julius O. Smith III ([email protected]) Center for Computer Research in Music and Acoustics (CCRMA) Input Forces u(t) Department of Music, Stanford University Stanford, California 94305 ft Model State x(t) x˙(t) February 5, 2019 Outline R State Space Models x˙(t)= ft[x(t),u(t)] • where Linear State Space Formulation • x(t) = state of the system at time t Markov Parameters (Impulse Response) • u(t) = vector of external inputs (typically driving forces) Transfer Function • ft = general function mapping the current state x(t) and Difference Equations to State Space Models inputs u(t) to the state time-derivative x˙(t) • Similarity Transformations The function f may be time-varying, in general • • t Modal Representation (Diagonalization) This potentially nonlinear time-varying model is • • Matlab Examples extremely general (but causal) • Even the human brain can be modeled in this form • 1 2 State-Space History Key Property of State Vector The key property of the state vector x(t) in the state 1. Classic phase-space in physics (Gibbs 1901) space formulation is that it completely determines the System state = point in position-momentum space system at time t 2. Digital computer (1950s) 3. Finite State Machines (Mealy and Moore, 1960s) Future states depend only on the current state x(t) • and on any inputs u(t) at time t and beyond 4. Finite Automata All past states and the entire input history are 5. -
Lecture 14 - AC Circuits, Resonance Y&F Chapter 31, Sec
Physics 121 - Electricity and Magnetism Lecture 14 - AC Circuits, Resonance Y&F Chapter 31, Sec. 3 - 8 • The Series RLC Circuit. Amplitude and Phase Relations • Phasor Diagrams for Voltage and Current • Impedance and Phasors for Impedance • Resonance • Power in AC Circuits, Power Factor • Examples • Transformers • Summaries Copyright R. Janow – Fall 2013 Current & voltage phases in pure R, C, and L circuits Current is the same everywhere in a single branch (including phase) Phases of voltages in elements are referenced to the current phasor • Apply sinusoidal voltage E (t) = EmCos(wDt) • For pure R, L, or C loads, phase angles are 0, +p/2, -p/2 • Reactance” means ratio of peak voltage to peak current (generalized resistances). VR& iR in phase VC lags iC by p/2 VL leads iL by p/2 Resistance Capacitive Reactance Inductive Reactance 1 V /i R Vmax /iC C Vmax /iL L wDL max R wDC Copyright R. Janow – Fall 2013 The impedance is the ratio of peak EMF to peak current peak applied voltage Em Z [Z] ohms peak current that flows im 2 2 2 Magnitude of Em: Em VR (VL VC) 1 Reactances: L wDL C wDC L VL /iL C VC /iC R VR /iR im iR,max iL,max iC,max For series LRC circuit, divide Em by peak current 2 2 1/2 Applies to a single Magnitude of Z: Z [ R (L C) ] branch with L, C, R VL VC L C Phase angle F: tan(F) see diagram VR R F measures the power absorbed by the circuit: P Em im Em im cos(F) • R ~ 0 tiny losses, no power absorbed im normal to Em F ~ +/- p/2 • XL=XC im parallel to Em F 0 Z=R maximum currentCopyright (resonance) R. -
ECE504: Lecture 2
ECE504: Lecture 2 ECE504: Lecture 2 D. Richard Brown III Worcester Polytechnic Institute 15-Sep-2009 Worcester Polytechnic Institute D. Richard Brown III 15-Sep-2009 1 / 46 ECE504: Lecture 2 Lecture 2 Major Topics We are still in Part I of ECE504: Mathematical description of systems model mathematical description → You should be reading Chen chapters 2-3 now. 1. Advantages and disadvantages of different mathematical descriptions 2. CT and DT transfer functions review 3. Relationships between mathematical descriptions Worcester Polytechnic Institute D. Richard Brown III 15-Sep-2009 2 / 46 ECE504: Lecture 2 Preliminary Definition: Relaxed Systems Definition A system is said to be “relaxed” at time t = t0 if the output y(t) for all t t is excited exclusively by the ≥ 0 input u(t) for t t . ≥ 0 Worcester Polytechnic Institute D. Richard Brown III 15-Sep-2009 3 / 46 ECE504: Lecture 2 Input-Output DE Description: Capabilities and Limitations Example: by˙(t) ay(t)+ = du(t)+ eu˙(t) cy¨(t) + Can describe memoryless, lumped, or distributed systems. + Can describe causal or non-causal systems. + Can describe linear or non-linear systems. + Can describe time-invariant or time-varying systems. + Can describe relaxed or non-relaxed systems (non-zero initial conditions). — No explicit access to internal behavior of systems, e.g. doesn’t directly to apply to systems like “sharks and sardines”. — Difficult to analyze directly (differential equations). Worcester Polytechnic Institute D. Richard Brown III 15-Sep-2009 4 / 46 ECE504: Lecture 2 State-Space Description: Capabilities and Limitations Example: x˙ (t) = Ax(t)+ Bu(t) y(t) = Cx(t)+ Du(t) — Can’t describe distributed systems.