DOCSLIB.ORG

Time Alignment)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Time Alignment) Measurement for Live Sound Welcome! Instructor: Jamie Anderson 2008 – Present: Rational Acoustics LLC Founding Partner & Systweak 1999 – 2008: SIA Software / EAW / LOUD Technologies Product Manager 1997 – 1999: Independent Sound Engineer A-1 Audio, Meyer Sound, Solstice, UltraSound / Promedia 1992 – 1997: Meyer Sound Laboratories SIM & Technical Support Manager 1991 – 1992: USC – Theatre Dept Education MFA: Yale School of Drama BS EE/Physics: Worcester Polytechnic University Instructor: Jamie Anderson Jamie Anderson [email protected] Rational Acoustics LLC Who is Rational Acoustics LLC ? 241 H Church St Jamie @RationalAcoustics.com Putnam, CT 06260 Adam @RationalAcoustics.com (860)928-7828 Calvert @RationalAcoustics.com www.RationalAcoustics.com Karen @RationalAcoustics.com and Barb @RationalAcoustics.com SmaartPIC @RationalAcoustics.com Support @RationalAcoustics.com Training @Rationalacoustics.com Info @RationalAcoustics.com What Are Our Goals for This Session? Understanding how our analyzers work – and how we can use them as a tool • Provide system engineering context (“Key Concepts”) • Basic measurement theory – Platform Agnostic Single Channel vs. Dual Channel Measurements Time Domain vs. Frequency Domain Using an analyzer is about asking questions . your questions Who Are You?" What Are Your Goals Today?" Smaart Basic ground rules • Class is informal - Get comfortable • Ask questions (Win valuable prizes!) • Stay awake • Be Courteous - Don’t distract! TURN THE CELL PHONES OFF NO SURFING / TEXTING / TWEETING PLEASE! Continuing Education" AKA: What can I do after I leave here? • RYM (RTM, RTFM) – For Smaart, read “Getting Started with Smaart v7” – Skim the help files in the program • Online at www.RationalAcoustics.com – Rational Support Forums – Rational Documentarium • Attend training sessions But most importantly, make measurements! Recommended Reading www.rationalacoustics.com/store/books" ***** SHAMELESS PLUG ******! The Only Math You Need Today: T= 1 ms T= .5 ms ƒ = 1 kHz ƒ = 2 kHz T=1/ƒ ƒ = 100 Hz ƒ = 250 Hz T= 10 ms & T= 4 ms ƒ = 20 Hz T= .1 ms ƒ = 500 Hz T= 50 ms ƒ = 10 kHz ƒ=1/T T= 2 ms T= .1 ms ƒ = 10 kHz Audio Frequency Spectrum Audible Range: 20 - 20,000 Hz (1000 x) or more realistically . 30 – 18,000 Hz (600 x) System Alignment" &" System Engineering What is wrong with this system?" Why doesn’t it sound good? Data is meaningless without CONTEXT! System engineering is managing interactions . with a goal. System Engineering / Alignment:" Goals • Engineering – Proper operation of equipment (Job #1) – Consistent coverage – Level / Power • Alignment (Voicing ?) – The system alignment needs to fit the goal • Tonality • Imaging • Remember - listening is the goal! Just Because You Can Operate a CAT Scan . ." Doesn’t Make You a Doctor! System Engineering / Alignment: Quay Koncepts • Systems Interact Most Where They are Equal Level • Solve the Problem at the Source • Use the Right Tool • 20% / 80% Managing Interactions: The Big Question + = ? or or Addition of Sine Waves! of Same Frequency and Equal Amplitude! Φ = 0 Degrees Complete Addition Φ = 90 Degrees Partial Addition Φ = 120 Degrees No Addition Φ = 180 Degrees Complete Cancellation Φ = 240 Degrees No Addition Example: 1 kHz Comb Filters: Addition of Signals of Varying Relative Level Equal Level 3 dB Level Diff. 6 dB Level Diff. System Engineering " Key Concept: • Interactions are greatest where signals are equal level - Crossover Points System Engineering " Key Concept: • Interactions are greatest where signals are equal level - Crossover Points – Relative phase governs the interaction – Phase Shift (Filters) – Polarity (Wiring) – Delay (Time Alignment) • Relative Level & Phase is KEY!!!!!!!! System Engineering " Key Concepts: • Solve problem at source – The closer to the source . the more effective the solution. Example: Source EQ X-over Amp Speaker The speaker you are measuring has a relatively flat response except: HF is 6 dB lower than LF above 1.6 kHz Target response: nominally flat Substantial HF noise 6dB 0dB 125 250 500 1k 2k 4k 8k -6dB Example: Source EQ X-over Amp Speaker The speaker you are measuring Potential Solutions has a relatively flat response EQ: except: Bring up HF on EQ HF is 6 dB lower than LF above 1.6 kHz Add extra HF to program material Substantial HF noise 6dB 0dB 125 250 500 1k 2k 4k 8k -6dB Example: Source EQ X-over Amp Speaker The speaker you are measuring Potential Solutions has a relatively flat response Level: except: Turn up HF on Amp HF is 6 dB lower than LF above 1.6 kHz Turn up HF at X-over output Substantial HF noise 6dB 0dB 125 250 500 1k 2k 4k 8k -6dB Example: Source EQ X-over Amp Speaker The speaker you are measuring Potential Solutions has a relatively flat response System Maintenance: except: Loss of 6 dB and in crease in HF noise a good HF is 6 dB lower than LF above 1.6 kHz indication of a bad cable on X-over HF out. Substantial HF noise 6dB 0dB 125 250 500 1k 2k 4k 8k -6dB One Bad Cable . System Engineering " Key Concepts: • Use the right tool – “Every item in your tool box is a hammer . except your wood chisels, they’re screwdrivers.” Tools in Order of Use • Acoustic Design //Treatment Treatment 1 • Equipment Choice / Maintenance • System Design - “Design to align” 2 • Level • Delay / Timing 3 • And lastly . EQ Example: Soluon(s) Our goal is to fix our system . ." . not the trace on the screen! Smaart is NOT a video game! System Engineering " Key Concepts: 20% / 80% – The first 20% of effort provides 80% of the benefits. – System alignment takes as much time you have. – Each task can take 100% of your time. Kinkade’s Korollary System Alignment & Smaart System Alignment: Is a Series of Decisions Made in Context It is Optimizing Your Compromises Smaart: Is an Analyzer . Is a Tool An analyzer is only a tool: " YOU make the decisions You decide what to measure. You decide which measurements to use. You decide what the resulting data means. And you decide what to do about it. System Alignment “Smaart-ing” a System System Engineering" &" The Serenity Prayer . Grant me the Serenity" To accept the things I cannot change… Courage to change the things I can, And Wisdom to know the difference." Measurements" &" Analyzers Analyzers • Analyzers are our tools for asking questions • Different measurements are good for asking different questions Our Two Basic Questions What do we have? What do we have, relative . Our System Measurement Model Input System Output Measurement Types: Single Channel vs. Dual Channel Input System Output • Single Channel: Signal Analysis What is the Input Signal? Absolute What is the Output Signal? Measured Directly • Dual Channel: System Analysis What did the system do to the signal passing through it? Relative - In vs Out Measured Indirectly Why Dual-Channel? The measurements have a reference – they can tell: • Arrival time of direct signal • Signal from noise • Direct from reflected … And they are Signal Independent Transfer Function System Input Signal Output Signal Measurement Channel (RTA) Transfer Function Reference Channel (RTA) Transfer Function System Input Signal Output Signal Measurement Channel (RTA) Transfer Function Reference Channel (RTA) Transfer Function System Input Signal Output Signal Measurement Channel (RTA) Transfer Function Reference Channel (RTA) Fourier Analysis • Jean Baptiste Joseph Fourier – All complex waves are composed of a combination of simple sine waves of varying amplitudes and frequencies Transforms A transform converts data from one domain to another. From one view to another Time Domain Freq. Domain (Amp vs Time) (Amp vs Freq) Waveform Spectrum FFT = Fast Fourier Transform What do you get if you transform a transfer function? • IFT produces impulse response Transfer Function . To . Impulse Response *So . If Frequency Response can be measured source independently - so can Impulse Response* How Smaart Works Input System Output Measurement Signal Reference Signal Wave How Smaart Works Input System Output FFT Spectrum Views RTA FFT Spectrograph Wave Spectrum How Smaart Works Input System Output FFT = FFT Transfer Function (Frequency Resp.) Wave Spectrum How Smaart Works Input System Output FFT IFT = FFT Transfer Function Impulse Resp. (Frequency Resp.) Wave Spectrum Time Domain vs. Freq. Domain Amplitude vs. Time Magnitude vs. Frequency FFT IFT Spectrum (RTA) Waveform Spectrum Amplitude vs. Time Magnitude and Phase vs. Frequency FFT IFT Response Analysis Signal Analysis Signal Analysis Response Dual Channel vs. SingleChannel vs. Dual Channel Impulse Response Frequency Response (Transfer Function) Basic Measurement Set-up Multi Measurement Set-up Thank You www.RationalAcoustics.com .
Load more

Recommended publications
  • J-DSP Lab 2: the Z-Transform and Frequency Responses
    J-DSP Lab 2: The Z-Transform and Frequency Responses Introduction This lab exercise will cover the Z transform and the frequency response of digital filters. The goal of this exercise is to familiarize you with the utility of the Z transform in digital signal processing. The Z transform has a similar role in DSP as the Laplace transform has in circuit analysis: a) It provides intuition in certain cases, e.g., pole location and filter stability, b) It facilitates compact signal representations, e.g., certain deterministic infinite-length sequences can be represented by compact rational z-domain functions, c) It allows us to compute signal outputs in source-system configurations in closed form, e.g., using partial functions to compute transient and steady state responses. d) It associates intuitively with frequency domain representations and the Fourier transform In this lab we use the Filter block of J-DSP to invert the Z transform of various signals. As we have seen in the previous lab, the Filter block in J-DSP can implement a filter transfer function of the following form 10 −i ∑bi z i=0 H (z) = 10 − j 1+ ∑ a j z j=1 This is essentially realized as an I/P-O/P difference equation of the form L M y(n) = ∑∑bi x(n − i) − ai y(n − i) i==01i The transfer function is associated with the impulse response and hence the output can also be written as y(n) = x(n) * h(n) Here, * denotes convolution; x(n) and y(n) are the input signal and output signal respectively.
    [Show full text]
  • Control Theory
    Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data.
    [Show full text]
  • The Scientist and Engineer's Guide to Digital Signal Processing Properties of Convolution
    CHAPTER 7 Properties of Convolution A linear system's characteristics are completely specified by the system's impulse response, as governed by the mathematics of convolution. This is the basis of many signal processing techniques. For example: Digital filters are created by designing an appropriate impulse response. Enemy aircraft are detected with radar by analyzing a measured impulse response. Echo suppression in long distance telephone calls is accomplished by creating an impulse response that counteracts the impulse response of the reverberation. The list goes on and on. This chapter expands on the properties and usage of convolution in several areas. First, several common impulse responses are discussed. Second, methods are presented for dealing with cascade and parallel combinations of linear systems. Third, the technique of correlation is introduced. Fourth, a nasty problem with convolution is examined, the computation time can be unacceptably long using conventional algorithms and computers. Common Impulse Responses Delta Function The simplest impulse response is nothing more that a delta function, as shown in Fig. 7-1a. That is, an impulse on the input produces an identical impulse on the output. This means that all signals are passed through the system without change. Convolving any signal with a delta function results in exactly the same signal. Mathematically, this is written: EQUATION 7-1 The delta function is the identity for ( ' convolution. Any signal convolved with x[n] *[n] x[n] a delta function is left unchanged. This property makes the delta function the identity for convolution. This is analogous to zero being the identity for addition (a%0 ' a), and one being the identity for multiplication (a×1 ' a).
    [Show full text]
  • 2913 Public Disclosure Authorized
    WPS A 13 POLICY RESEARCH WORKING PAPER 2913 Public Disclosure Authorized Financial Development and Dynamic Investment Behavior Public Disclosure Authorized Evidence from Panel Vector Autoregression Inessa Love Lea Zicchino Public Disclosure Authorized The World Bank Public Disclosure Authorized Development Research Group Finance October 2002 POLIcy RESEARCH WORKING PAPER 2913 Abstract Love and Zicchino apply vector autoregression to firm- availability of internal finance) that influence the level of level panel data from 36 countries to study the dynamic investment. The authors find that the impact of the relationship between firms' financial conditions and financial factors on investment, which they interpret as investment. They argue that by using orthogonalized evidence of financing constraints, is significantly larger in impulse-response functions they are able to separate the countries with less developed financial systems. The "fundamental factors" (such as marginal profitability of finding emphasizes the role of financial development in investment) from the "financial factors" (such as improving capital allocation and growth. This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to study access to finance. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Kari Labrie, room MC3-456, telephone 202-473-1001, fax 202-522-1155, email address [email protected]. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at [email protected] or [email protected]. October 2002. (32 pages) The Policy Research Working Paper Series disseminates the findmygs of work mn progress to encouirage the excbange of ideas about development issues.
    [Show full text]
  • Mathematical Modeling of Control Systems
    OGATA-CH02-013-062hr 7/14/09 1:51 PM Page 13 2 Mathematical Modeling of Control Systems 2–1 INTRODUCTION In studying control systems the reader must be able to model dynamic systems in math- ematical terms and analyze their dynamic characteristics.A mathematical model of a dy- namic system is defined as a set of equations that represents the dynamics of the system accurately, or at least fairly well. Note that a mathematical model is not unique to a given system.A system may be represented in many different ways and, therefore, may have many mathematical models, depending on one’s perspective. The dynamics of many systems, whether they are mechanical, electrical, thermal, economic, biological, and so on, may be described in terms of differential equations. Such differential equations may be obtained by using physical laws governing a partic- ular system—for example, Newton’s laws for mechanical systems and Kirchhoff’s laws for electrical systems. We must always keep in mind that deriving reasonable mathe- matical models is the most important part of the entire analysis of control systems. Throughout this book we assume that the principle of causality applies to the systems considered.This means that the current output of the system (the output at time t=0) depends on the past input (the input for t<0) but does not depend on the future input (the input for t>0). Mathematical Models. Mathematical models may assume many different forms. Depending on the particular system and the particular circumstances, one mathemati- cal model may be better suited than other models.
    [Show full text]
  • Finite Impulse Response (FIR) Digital Filters (I) Types of Linear Phase FIR Filters Yogananda Isukapalli
    Finite Impulse Response (FIR) Digital Filters (I) Types of linear phase FIR filters Yogananda Isukapalli 1 Key characteristic features of FIR filters 1. The basic FIR filter is characterized by the following two equations: N -1 N -1 y(n) = å h(k)x(n - k) (1) H (z) = å h(k)z -k (2) k =0 k =0 where h(k), k=0,1,…,N-1, are the impulse response coefficients of the filter, H(z) is the transfer function and N the length of the filter. 2. FIR filters can have an exactly linear phase response. 3. FIR filters are simple to implement with all DSP processors available having architectures that are suited to FIR filtering. 2 Linear phase response • Consider a signal that consists of several frequency components passing through a filter. 1. The phase delay (T p ) of the filter is the amount of time delay each frequency component of the signal suffers in going through the filter. 2. The group delay (T g ) is the average time delay the composite signal suffers at each frequency. 3. Mathematically, T p = -q (w) / w (3) Tg = -dq (w) / dw (4) where q(w) is the phase angle. 3 • A filter is said to have a linear phase response if, J(w) = -aw (5) J(w) = b -aw (6) where a and b are constants. Example) Ideal Lowpass filter k Magnitude 0 wc p w Phase 0 a wc p w 4 k e-jwa passband jw H(e ) = 0 otherwise Magnitude response = |H(ejw)| = k Phase response (q(w)) = < H(ejw) = -wa Follows: y[n] = kx[n-a] : Linear phase implies that the output is a replica of x[n] {LPF} with a time shift of a - p -wu -wl 0 wl wu p w - p -wu -wl 0 wl wu p w 5 Linear phase FIR filters • Symmetric impulse response will yield linear phase FIR filters.
    [Show full text]
  • Vector Autoregressions
    Vector Autoregressions • VAR: Vector AutoRegression – Nothing to do with VaR: Value at Risk (finance) • Multivariate autoregression • Multiple equation model for joint determination of two or more variables • One of the most commonly used models for applied macroeconometric analysis and forecasting in central banks Two‐Variable VAR • Two variables: y and x • Example: output and interest rate • Two‐equation model for the two variables • One‐Step ahead model • One equation for each variable • Each equation is an autoregression plus distributed lag, with p lags of each variable VAR(p) in 2 riablesaV y=t1μ 11 +α yt− 1+α 12 yt− +L 2 +αp1 y t− p +11βx +t− 1 β 12t x− +L 1 βp +1x t− p1 e t + x=t 2μ 21+α yt− 1+α 22 yt− +L 2 +αp2 y t− p +β21x +t− 1 β 22t x− +L 1 β +p2 x t− p e2 + t Multiple Equation System • In general: k variables • An equation for each variable • Each equation includes p lags of y and p lags of x • (In principle, the equations could have different # of lags, and different # of lags of each variable, but this is most common specification.) • There is one error per equation. – The errors are (typically) correlated. Unrestricted VAR • An unrestricted VAR includes all variables in each equation • A restricted VAR might include some variables in one equation, other variables in another equation • Old‐fashioned macroeconomic models (so‐called simultaneous equations models of the 1950s, 1960s, 1970s) were essentially restricted VARs – The restrictions and specifications were derived from simplistic macro theory, e.g.
    [Show full text]
  • Lecture 15- Impulse Reponse and FIR Filter
    In this lecture, we will learn about: If we apply an impulse at the input of a system, the output response is known as the 1. Impulse response of a discrete system and what it means. system’s impulse response: h(t) for continuous time, and h[n] of discrete time. 2. How impulse response can be used to determine the output of the system given We have demonstrated in Lecture 3 that the Fourier transform of a unit impulse is a its input. constant (of 1), meaning that an impulse contains ALL frequency components. 3. The idea behind convolution. Therefore apply an impulse to the input of a system is like applying ALL frequency 4. How convolution can be applied to moving average filter and why it is called a signals to the system simultaneously. Finite Impulse Response (FIR) filter. Furthermore, since integrating a unit impulse gives us a unit step, we can integrate 5. Frequency spectrum of the moving average filter the impulse response of a system, and we can obtain it’s step response. 6. The idea of recursive or Infinite Impulse Response (IIR) filter. In other words, the impulse response of a system completely specify and I will also introduce two new packages for the Segway proJect: characterise the response of the system. 1. mic.py – A Python package to capture data from the microphone So here are two important lessons: 2. motor.py – A Python package to drive the motors 1. Impulse response h(t) or h[n] characterizes a system in the time-domain.
    [Show full text]
  • Transfer Functions 20.6   
    ® Transfer Functions 20.6 Introduction In this Section we introduce the concept of a transfer function and then use this to obtain a Laplace transform model of a linear engineering system. (A linear engineering system is one modelled by a constant coefficient ordinary differential equation.) We shall also see how to obtain the impulse response of a linear system and hence to construct the general response by use of the convolution theorem. ' • be able to use the convolution theorem $ • be familiar with taking Laplace transforms Prerequisites and inverse Laplace transforms Before starting this Section you should ... • be familiar with the delta (impulse) function and its Laplace transform '& • find a transfer function of a linear system $% • show how some linear systems may be Learning Outcomes combined together by combining appropriate transfer functions On completion you should be able to ... • obtain the impulse response and the general response to a linear engineering system & % HELM (2008): 63 Section 20.6: Transfer Functions 1. Transfer functions and linear systems Linear engineering systems are those that can be modelled by linear differential equations. We shall only consider those sytems that can be modelled by constant coefficient ordinary differential equations. Consider a system modelled by the second order differential equation. d2y dy a + b + cy = f(t) dt2 dt in which a, b, c are given constants and f(t) is a given function. In this context f(t) is often called the input signal or forcing function and the solution y(t) is often called the output signal. We shall assume that the initial conditions are zero (in this case y(0) = 0, y0(0) = 0).
    [Show full text]
  • Linear Filters 1
    Linear Filters 1. Convolutions and filters. A filter is a “black box” that takes an input signal, processes§ it, and then returns an output signal that in some way modifies the input. For example, if the input signal is noisy, then one would want a filter that removes noise, but otherwise leaves the signal unchanged. From a mathematical point of view, a signal is a function f : R C that is piecewise contiuous, and perhaps satisfies other properties, and a filter is a transformation→ L that maps a signal f into another signal f˜. The space of signals is a vector space. L is a linear filter if it is a linear transformation on the space of signals—i.e., it satisfies these two properties: (i) Additivity: L[f + g] = L[f] + L[g] (ii) Homogeneity: L[cf] = cL[f], where c is a constant. There is another property that we want our “black box” L to have. If we play an old, scratchy record for half an hour starting at 3 pm today, and put the signal through a noise reducing filter, we will hear the cleaned-up output, at roughly the same time as we play the record. If we play the same record at 10 am tomorrow morning, and use the same filter, we should hear the identical output, again at roughly the same time. This property is called time invariance. It can be precisely formulated this way. Let f(t) be the original signal, and let L[f(t)] = f˜(t) be the filtered output. L is said to be time-invariant if L[f(t a)] = f˜(t a) for any a.
    [Show full text]
  • Shock Elasticities and Impulse Responses
    Shock Elasticities and Impulse Responses Jaroslav Boroviˇcka Lars Peter Hansen New York University University of Chicago and NBER [email protected] [email protected] Jos´eA. Scheinkman Columbia University, Princeton University and NBER [email protected] April 14, 2014 Abstract We construct shock elasticities that are pricing counterparts to impulse response functions. Recall that impulse response functions measure the importance of next- period shocks for future values of a time series. Shock elasticities measure the contri- butions to the price and to the expected future cash flow from changes in the exposure to a shock in the next period. They are elasticities because their measurements com- pute proportionate changes. We show a particularly close link between these objects in environments with Brownian information structures. There are several alternative ways in which one may approach the impulse prob- lem .... One way which I believe is particularly fruitful and promising is to study what would become of the solution of a determinate dynamic system if it were exposed to a stream of erratic shocks that constantly upsets the continuous evolu- tion, and by so doing introduces into the system the energy necessary to maintain the swings. Frisch (1933) 1 Introduction Impulse response function characterize the impact of “a stream of erratic shocks” on dy- namic economic models. They measure the consequences of alternative shocks on the future variables within the dynamical system. These methods are routinely used in linear time series analysis, and they can be generalized to nonlinear environments. See Gallant et al. (1993), Koop et al. (1996), and Gourieroux and Jasiak (2005) for nonlinear extensions.
    [Show full text]
  • Lecture 12 AO Control Theory
    Lecture 12 AO Control Theory Claire Max with many thanks to Don Gavel and Don Wiberg UC Santa Cruz February 18, 2016 Page 1 What are control systems? • Control is the process of making a system variable adhere to a particular value, called the reference value. • A system designed to follow a changing reference is called tracking control or servo. Page 2 Outline of topics • What is control? - The concept of closed loop feedback control • A basic tool: the Laplace transform - Using the Laplace transform to characterize the time and frequency domain behavior of a system - Manipulating Transfer functions to analyze systems • How to predict performance of the controller Page 3 Adaptive Optics Control Aberrated wavefront phase surfaces! Telescope Aperture! Wavefront sensor! Corrected Wavefront! Computer! Imaging Wavefront corrector dector! (Deformable Mirror)! 3! Page 4 Differences between open-loop and closed-loop control systems • Open-loop: control system OPEN uses no knowledge of the output • Closed-loop: the control action is dependent on the output in some way • “Feedback” is what CLOSED distinguishes open from closed loop • What other examples can you think of? Page 5 More about open-loop systems • Need to be carefully calibrated ahead of time: OPEN • Example: for a deformable mirror, need to know exactly what shape the mirror will have if the n actuators are each driven with a voltage Vn • Question: how might you go about this calibration? Page 6 Some Characteristics of Closed- Loop Feedback Control • Increased accuracy (gets to the desired final position more accurately because small errors will get corrected on subsequent measurement cycles) • Less sensitivity to nonlinearities (e.g.
    [Show full text]