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`EXHIBIT
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`
`ClARENCE W. deSilYA
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 1
`
`

`

`Control Sensors
`and Actuators
`
`CLARENCE W. DE SILVA
`
`~
`
`.
`~ !:
`
`.
`
`.1
`
`.
`
`~RENTICE HALL, Englewood Cliffs, New Jersey 07632
`
`JAMES WHIT£ LIBRARY
`ANDREWS UNIVERSI1Y
`BERRIEN SPRINGS, Ml 4910<'1
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 2
`
`

`

`De Silva, Clarence W.
`Control Sensors and actuators.
`Include~ bibliographies and index.
`l. Automatic control. 2. Detectors. 3. Actuators.
`L Title.
`TJ213.D38 1989
`TSBN 0-13-171745-6
`
`!18-2!!998
`
`629.8
`
`To Charmaine, CI, and Cheryl-as their senses develop
`and as they becume increasingly active.
`
`I
`
`.I .,
`
`Editorial/production supervision and
`interior design: David Ershun
`Cover design: Ben Santora
`Manufacturing huycr: Mary Ann Gloriandc
`
`•
`
`© J9il9 hy Prentice-Hall, Inc.
`A Division of Simon & Schuster
`Englewood Cliffs, New Jersey 07632
`
`All rights reserved. No part of this book may be
`reproduced, in any fonn or by any means,
`without pcrmis~ion in writing from the publi.~her.
`
`The publisher offers discounts on this book when ordered
`in hulk quantities. For more information, write:
`
`Special Sales/College Marketing
`Prentice Hall
`College Technical and Reference Division
`Englewood Cliffs, NJ 07632
`
`Printed in the United States of America
`
`109 i l765432
`
`ISBN D-13-171745-6
`
`'
`i
`
`/
`
`.·) .·.~-
`. )'..;
`
`PRENTICI:!-HALL [NTERNATIONAL (UK) L!MJ'J£D, London
`PRENTIC!',HAt.L Of! AUSTRALIA l>rY. LIMITED, Sydney
`PRENTICE-HALl. CANADA INC., Toronto
`PRENTICE-HALL HISPANUAMERICANA, S.A., Mexico
`PRENT!Cf.-HALL OF INDIA PRIVATE LIMITED, New De/hi
`PRENTICE-HALL Of JAPAN, INC., Tokyo
`SIMON II< SCHUSTER ASIA PrE. LID., Sin11apore
`EDITORA PRF.NTIC:E-HAI.I. nn BRA~!!., LmA., Rio de Janeiro
`
`513414
`
`1(
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 3
`
`

`

`Contents
`
`~-
`
`· ....
`
`PREFACE
`
`1 CONTROL, INSTRUMENTATION. AND DESIGN
`
`1.1
`
`1.2
`
`1.3
`
`1.4
`1.5
`
`1.6
`
`1.7
`
`Introduction
`
`l
`
`Control Sy~tem Architecture 2
`
`Digital Control 3
`
`Signal Classification in Control Systems
`
`5
`
`Advantages of Digital Control 8
`
`Feedforward Control 9
`
`Instrumentation and Design
`
`11
`
`Problems 12
`References 16
`
`2 PERFORMANCE SPECIFICATION AND
`COMPONENT MATCHING
`
`2.1
`
`2. 2
`
`Introduction 17
`
`Sensors and Transducers
`
`I 9
`
`vii
`
`1
`
`17
`
`iii
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`

`

`'·I
`
`2.3
`
`2.4
`
`2.5
`
`2.6
`
`2. 7
`
`Transfer Function Models for Transducers 20
`
`Parameters for Performance Specification 26
`
`Impedance Characteristics 36
`
`lnsuument Ratings 50
`
`Error Analy~is 55
`
`Problems 75
`
`References 83
`
`3 ANALOG SENSORS FOR MOTION MEASUREMENT
`
`84
`
`3.1
`
`3.2
`
`3.3
`
`3.4
`
`3.5
`
`3.6
`
`3.7
`
`3. 8
`
`3.9
`
`Introduction 84
`
`Motion Transducers 85
`
`Potentiometers 87
`
`Variable-Inductance Transducers 95
`
`Permanent-Magnet Transducers 108
`
`Eddy Current Transducers 112
`
`Variable-Capacitance Transducers 113
`
`Piezoelectric Transducers
`
`118
`
`Other Types of Sensors 126
`
`3.10 A Design Criterion for Control Systems 132
`
`Problems 134
`
`References 142
`
`4 TORQUE, FORCE, AND TACTILE SENSORS
`
`144
`
`Introduction 144
`
`Force Control 145
`
`Strain Gages 153
`
`Semiconductor Strain Gages 167
`
`Torque Sensors 174
`
`Force Sensors 196
`
`Tactile Sensing 199
`
`4.1
`
`4.2
`
`4.3
`
`4.4
`
`4.5
`
`4.6
`
`4.7
`
`iv
`
`Contents
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`

`

`84
`
`144
`
`Problems 206
`
`References 216
`
`5 DIGITAL TRANSDUCERS
`
`218
`
`5.1
`
`5.2
`
`5. 3
`
`5.4
`
`5.5
`
`5.6
`
`.5. 7
`
`5.S
`
`5.9
`
`Introduction 218
`
`Shaft Encoders 219
`
`Incremental Optical Encoders 225
`
`Absolute Optical Encoders 235
`
`Encoder Error 238
`
`Digital Resolvers 243
`
`Digital Tachometer8 244
`
`Hall Effect Sensors 245
`
`Measurement of Translatory Motions 247
`
`5.10
`
`Limit Switches 249
`
`Problems 249
`
`References 252
`
`6 STEPPER MOTORS
`
`253
`
`6.1
`
`6.2
`
`6.3
`
`6.4
`
`6.5
`
`6.6
`
`6. 7
`
`6.8
`
`6.9
`
`Introduction 253
`
`Principle of Operation 254
`
`Stepper Motor Classification 262
`
`Single-Stack Stepper Motors 263
`
`Multiple-Stack Stepper Motors 273
`
`Open-Loop Control of Stepping Motors 275
`
`Stepper Motor Response 277
`
`Static Position Error 284
`
`Damping of Stepper Motors 286
`
`6. l 0
`
`Feedback Control of Stepper Motors 293
`
`6.11
`
`Stepper Motor Models 299
`
`6.12
`
`Stepper Motor Selection and Applications 304
`
`Contents
`
`Contents
`
`v
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`

`

`Problems 311
`References 322
`
`7 CONTINUOUS-DRIVE ACTUATORS
`
`323
`
`Introduction 323
`7.1
`DC Motors 324
`7.2
`Bmshless DC Motors 327
`7.3
`DC Motor Equations 330
`7.4
`Control of DC Motors 342
`7.5
`Torque Motors 359
`7.6
`Motor Selection Considerations 363
`7.7
`Induction Motors 365
`7.8
`Induction Motor Control 375
`7.9
`Synchronous Motors 387
`7.10
`7.ll Hydraulic Actuators 390
`7.12 Hydraulic Control Systems 401
`Problems 416
`References 428
`
`ANSWERS TO NUMERICAL PROBLEMS
`
`INDEX
`
`429
`
`431
`
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`
`vi
`
`Contents
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`-~
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`·~
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`;:
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 7
`
`

`

`7
`Control,
`Instrumentation, and
`Design
`
`1.1 INTRODUCTION
`
`The demand for servnmechanisms in military applications during World War II pro(cid:173)
`vided much incentive and many resources for the growth of control technology.
`Early efforts were devoted to the development of analog controllers, which are elec(cid:173)
`tronic devices or circuits that generate proper drive signals for a plant (process). Par(cid:173)
`allel advances were necessary in actuating devices such a~ motors, solenoids, and
`valves that drive the plant. For feedback control, further developments in sensors
`and transducers became essential. With added ~nphistication in control systems, it
`was soon apparent that analog control techniques had serious limitations. In particu(cid:173)
`lar, linear assumptions were used to develop controllers even for highly nonlinear
`plants. Furthermore, complex and costly circuitry was often needed to generate even
`simple control signals. Consequently, mnst analog controllers were limited to on/off
`and proportional-integral-derivative (PID) actions, and lead and lag compensation
`networks were employed to compenstate for weaknesses in such simple contrnl ac(cid:173)
`tions.
`The digital computer, first developed for large number-crunching jobs, was
`employed as a contrnller in complex control systems in the 1950s and 1960s. Origi(cid:173)
`nally, cost constraints restricted its use primarily to aerospace applications that re(cid:173)
`quired the manipulation of large amounts of data (complex models, several hundred
`signals, and thousands of system parameters) for control and that did not 1ace serious
`cost restraints. Real-time contrnl requires fast computatinn, and this speed of com(cid:173)
`putation is determined by the required control bandwidth (or the speed of control)
`and parameters (e.g., time constants, natural frequencies, and damping constants) of
`the process that is being controlled. For instance, prelaunch monitoring and control
`of a space vehicle would require digital data acquisition at very high sampling rates
`(e.g., 50,000 samples/second). As a result of a favorable decline of computation cost
`(both hardware and software) in subsequent years, widespread application of digital
`computers as control devices (i.e., digital control) has become feasible. Dramatic
`developments in large-scale integration (LSI) technology and microprocessors in the
`
`1
`
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`

`

`1970s resulted in very significant drops in digital processing costs, which made digi(cid:173)
`tal control a very attractive alternative to analog cnntrol. Today, digital control has
`become an integral part of numerous systems and applications, including machine
`tools, robotic manipulators, automobiles, aircraft autopilots, nuclear power plants,
`traffic control systems, and chemical process plants.
`Control engineers should be able to identify or sclect components for a control
`system, mndel and analyze individual components or overall systems, and choose
`parameter values so as to perform the intended functions of the particular system in
`accordance with some specifkations. Component identilkation, analysis, selection,
`matching and intertacing, and system tuning (adjusting parameters to obtain the re(cid:173)
`quired response) are essential tasks in the instrumentatinn and design of a control
`system.
`
`1.2 CONTROL SYSTEM ARCHITECTURE
`
`Let us examine the generalized cnntrol system represented by the block diagram in
`figure 1. I. We have identified several discrete blocks, depending on various func(cid:173)
`tions that take place in a typical contrnl system. Before proceeding, we must keep in
`mind that in a practical control system, this type of clear demarcation of components
`might be difficult; one piece of hardware might perform several functions, or more
`than one distinct unit of equipment might be associated with one function. Neverthe(cid:173)
`less, figure 1.1 is useful in understanding the architecture of a general control sys(cid:173)
`tem. This is an analog control system because the associated signals depend on the
`continuous time variable; no signal sampling or data encoding is involved in the sys-
`tem.
`
`Plant is the system or "process" that we are interested in controlling. By con(cid:173)
`trol, we mean making the system respond in a desired manner. To be able tn accom(cid:173)
`plish this, we must have access to the drive system or actuator of the plant. We apply
`certain command signals, or input, to the controller and expect the plant to behave
`in a desirable manner: This is the open-loop control situation. In this case, we do not
`use current information on ~·ystem response to determine the control signals. Infeed(cid:173)
`back control systems, the control loop has to be closed; clo~·ed-loop control means
`making measurements of system response and employing that information to gener(cid:173)
`ate control signals so as to correct any output errors. The output measurements are
`made primarily using analog devices, typically consisting of sensor-transducer units.
`
`Reference
`Input Signal
`
`1---.--...... 0utputs
`
`Figure l.I. Components of a typical analog control system.
`
`-·:
`
`-~~: ¥ I 9:
`1
`
`~
`~
`i\1
`
`1.3 DIGI.J
`J
`I$
`tJ
`tH
`f3 f:'
`a~
`re
`it
`&~
`d~
`ti
`s6
`'~ N
`
`2
`
`Control, Instrumentation, and Design
`
`Chap. 1
`
`s~
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 9
`
`

`

`ligi(cid:173)
`haS
`hine
`wts,
`
`ntro1
`oose
`min
`tion,
`e rc(cid:173)
`ntrol
`
`1m in
`tunc(cid:173)
`~ep in
`ments
`more
`erthe(cid:173)
`>l sys(cid:173)
`)ll the
`:e sys-
`
`y con(cid:173)
`ccom-
`apply
`Jehave
`do not
`lfeed(cid:173)
`means
`gener(cid:173)
`Jts are
`·units.
`
`-Outputs
`
`An important factor.that we must consider in any practical control system is noise,
`including external disturbances. Noise may represent actual contamination of signals
`or the presence of other unknowns, uncertainties, and errors, such as parameter
`variations and modeling errors. Furthermore, weak signals will have to be am(cid:173)
`plified, and the form of a signal might have to be modified at various points of inter(cid:173)
`action. In these respects, signal-conditioning methods such as filtering, amplification,
`and modulation become important.
`ldentilication of the hardware components (perhaps commercially available
`off-the-shelf items) corresponding to each functional block in figure 1.1 i11 one of the
`first steps of instrumentation. For example, in process control applications off-the(cid:173)
`shelf analog proportional-integral-derivative (PID) controllers may be used. These
`controllers for process control applications have knobs or dials for control parameter
`settings-that is, proportional band or gain, reset rate (in repeats of the proportional
`action per unit time), and rate time constant. The control bandwidth (frequency
`range of operation) of these devices is specified. Various control modes-such as
`on/off, proportional, integral, and derivative, or combinatinns-are provided by the
`same control box.
`Actuating devices (actuators) include DC motors, AC motors, stepper motors,
`solenoids, valves, and relays, which are also commercially available to various
`spedfications. Potentiometers, differential transformers, resolvers, synchros, strain
`gauges, tachometers, piezoelectric devices, thermocouples, thermistors, and resis(cid:173)
`tance temperature detectors (RfDs) are examples of sensors used to measure process
`response for monitoring performance and possible feedback. Charge amplifiers,
`lock-in amplifiers, power amplifiers, switching amplifiers, linear amplifiers, tracking
`filters, low-pass filters, high-pass filters, and notch filters are some of the signal(cid:173)
`conditioning devices used in analog control systems. Additional components, such
`as power supplies and surge-protection units, are often needed in control, but they
`are not indicated in figure l.l because they are only indirectly related to control
`functions. Relays and other switching devices and modulators and demodulators may
`also be included.
`
`1.3 DIGITA'- CONTROL
`
`Direct digital control (DDC) systems are quite similar to analog control systems.
`The main difference in a DDC system is that a digital computer takes the place of
`the analog controller in figure l.l. Control computers have to be dedicated machines
`for real-time operation where processing has to be synchronized with plant operation
`and actuation requirements. This also requires a real-time clock. Apart from these
`requirements, control computers are basically no different from general-purpose dig(cid:173)
`ital computers. They consist of a processor to perform computations and to oversee
`data transfer, memory for program and data storage during processing, mass storage
`devices to store information that is not immediately needed, and input/output devices
`to read in and send out information. Digital control systems might utilize digital in(cid:173)
`struments and additional processors for actuating, signal-conditioning, or measuring
`functions, as well. For example, a stepper motor that responds with incremental mo-
`
`Chap. 1
`
`Sec. 1.3
`
`Digital Control
`
`3
`
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`
`

`

`tion steps when driven by pulse signals can be considered a digital actuator. Further(cid:173)
`more, it usually contains digital logic circuitry in its drive system. Similarly, a two(cid:173)
`position solenoid is a digital (binary) actuator. Digital flow control may be
`accomplished using a digital control valve. A typical digital valve comists of a
`bank of orillces, each sized in proportion to a place value of a binary word
`(2', i = 0, I, 2, ... , n). Each orifice is actuated by a separate rapid-acting on/off so(cid:173)
`lenoid. In this manner, many digital combinations of flow values can be obtained.
`Direct digital measurement of displacements and velocities can be made using shaft
`encoders. These are digital transducers that generate coded outputs (e.g., in binary
`or gray-scale representation) or pulse signals that can be coded using counting cir(cid:173)
`cuitry. Such outputs can be read in by the control computer with relative ease. Fre(cid:173)
`quency counters al~o generate digital signals that can be fed directly into a digital
`controller. When measured signals are in the analog form, an analog front end is
`necessary to interlace the transducer and the digital controller. Jnput/ontput interface
`boards that can take both analog and digital signals are available with digital con(cid:173)
`trollers.
`A block diagram of a direct digital control system is shown in figure 1.2. Note
`that the functions of this control system are quite similar to those shown in figure
`1.1 for an analog control system. The primary difference is the digital controller
`(processor), which is used to generate the control signals. Therefore, analog mea(cid:173)
`surements and reference signals have to be sampled and encoded prior to digitaL pro(cid:173)
`cessing within the controller. Digital processing can be conveniently used for signal
`conditioning as well. Alternatively, digital signal processing (DSP) chips can func(cid:173)
`tion as digital controllers. However, analog signals are preconditioned, using analog
`circuitry prior to digitizing in order to eliminate or minimize problems due to alias(cid:173)
`ing distortion (high-frequency components above half the sampling frequency ap(cid:173)
`pearing as low-frequency components) and leakage (error due to signal truncation)
`as well as to improve the signal level and filter out extraneous noise. The drive sys-
`
`Reference input
`
`!--,,-___..._Outputs
`
`Figure 1.2. Block diagram of a direct digital control system.
`
`4
`
`Control, Instrumentation, and Design
`
`Chap. 1
`
`BNA/Brose Exhibit 1065
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`
`

`

`tern of a plant typically takes in analog signals. Often, the digital output from con(cid:173)
`troller has to be converted into analog form for this reason. Both analog-to-digital
`conversion (ADC) and di8ital-to-analog conversion (DAC) ~:an be interpreted as
`signal-conditioning (modification) procedures. If more than one output signal is
`measured, cad1 signal will have to be conditioned and processed separately. Ideally,
`this will require separate conditioning and processing hardware for each signal chan(cid:173)
`nel. A less expensive (but slower) alternative would be to time-share this expensive
`equipment by using a multiplexer. This device will pick one channel of data from a
`bank of data channels in a sequential manner and connect it to a common input
`device. Both analog and digital multiplexers are available. In a digital multiplexer,
`the input signals come from a bank of digital sensors, and the output signal itself,
`which would be in digital form, goes directly into the digital controller. High-speed
`multiplexers (e.g., over 50,000 switchings/second) use electronic switching.
`For complex processes with a large number of input/output variables (e.g., a
`nuclear power plant) and with systems that have various operating requirements
`(e.g., the space shuttle), centralized direct digital control is quite difficult to imple(cid:173)
`ment. Some form of distributed control is appropriate in large systems >uch as man(cid:173)
`ufacturing cells, fdctorie.~. and multicomponent process plants. A tavorite distributed
`control architecture is provided by heirarchical control. Here, distribution of control
`is available both geographically and functionally. An example for a three-level hier(cid:173)
`archy is shown in figure 1.3. Management decisions, supervisory control, and coor(cid:173)
`dination between plants are provided by the management (supervisory) computer,
`which is at the highest level (level3) of the hierarchy. The next lower level computer
`generates control settings (or reference inputs) tor each control region in the corre(cid:173)
`sponding plant. Set points and reference signals are inputs to the direct digital con(cid:173)
`trol (DDC) computers that control each control region. The computers communicate
`using a suitable information network. Information transfer in both directions (up and
`down) should he possible for best performance and flexibility. In master-slave dis(cid:173)
`tributed control, only downloading of information is available.
`
`1.4 SIGNAL CLASSIFICATION IN CONTROL SYSTEMS
`
`A digital control system can be loosely interpreted as one that uses a digital com(cid:173)
`jXlter as the controller. It is more appropriate, however, to understand the nature of
`the signals that are present in a control system when identifying it as a digital con(cid:173)
`trol system.
`Ana/of? signals are continuous in time. They are typically generated as outputs
`of a dynamic system. (Note that the dynamic system could be a signal generator or
`any other device, equipment, or physical system.) Analytically, analog signals are
`represented as functions of the continuous time variable t.
`Sampled data are, in fuct, pulse amplitude-modulated signals. In this case, in(cid:173)
`tormation is carried by the amplitude of each pulse, with the width of the pulses
`kept constant. For comtant sampling rate, the distance between adjacent pulses
`is also kept constant. In a physical situation, a pulse amplitude-modulated signal is
`generated through a sample-and-hold operation, in which the signal is sampled at
`
`Sec. 1.4
`
`Signal Classification in Control Systems
`
`5
`
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`Page 12
`
`

`

`------ ------· ----···--·----··------
`
`Geographic Distribution
`
`---+-
`
`Level 3
`
`Supervisory
`(Management)
`Computer
`
`Process Plant 1
`
`Level 2
`
`Control-Setting
`Computer
`
`Local Control System 1
`
`Set Points
`(Reference Inputs)
`
`Functional i
`Distribution '
`
`Levell
`
`f---.---;.- 0 utputs
`
`Feedback
`Signals
`
`Figure 1.3. A three-level hierarchical control scheme.
`
`the beginning of the sampling period and kept constant at that value, irrespective of
`the true value of the signal over that period. An important advantage of sampling is
`that expensive equipment can be shared among many signals. Furthermore, sam(cid:173)
`pling is necessary in real-time digital processing to allow for the processing time.
`Analytically, sampled data consist of a sequence of numbers (or a function of integer
`variable).
`Digital data are coded numerical data. For example, a binary code or ASCII
`(American Standard Code for Information Interchange) may be used to represent
`each value in a sequence of digital data. The code itself determines the actual value
`of a particular unit of digital information. Typically, digital data are generated by
`digital processors, digital transducers, counters, encoders, and other such digital
`devices.
`Table 1.1 summarizes the identifying characteristics of these three types of
`data. Analog systems generate analog signals only. Sampled-data systems depend on
`analog data as well as sampled data. Digital systems, however, utilize all three types
`of signals, generally at different levels of interaction. Sampled-data ~-ystems and dig(cid:173)
`ital systems may be modeled using discrete-time mode!J> (see table 1.2).
`
`6
`
`Control, Instrumentation, and Design
`
`Chap. 1
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 13
`
`

`

`TABLE 1.1 SIGNAL CATEGORIES FOR IDENTIFYING CONTROL SYSTEM TYPES
`
`Signal (data)
`category
`
`Analog .~ignals (data)
`
`Sampled duta
`
`Digital data
`
`Description
`
`Continuou~ in timet; typically represents an output of a dynamic
`system
`Pulse amplitude, modulated signals
`Information carried by pulse amplitude
`Typically generated by ~ample-and-hold pro'e~s
`Coded numerical data; the particular mde determines the numerical
`value
`Typically generated by digital processors, digital transducers, and
`counters
`
`TABLE 1.2 REPRESENTATIVE ANALYTICAL CHARACTERISTICS OF
`CONTINUOUS-TIME AND DISCRETE-TIME SYSTEMS
`
`Analytical model
`
`System
`
`Time domain
`
`Transfer-function domain
`
`C11ntinuous-time
`')'Stems
`
`Discrete-time
`systems
`
`Differenti~l
`equations
`
`Difference
`equation~
`
`Laplace transfer functions
`or Fourier frequency response
`functions
`Z-transform tran.~fcr function~
`
`Example 1.1
`The sampling period ilT for data acquisition is an important parameter in real-time dig(cid:173)
`ital control. Discuss the significance of the sampling period.
`
`Solution On the one hand, tJ.T has to. be sufficiently large so that required processing
`and data tr<~nsfer can be done during that time for each control step. This is crucial in
`real-time control. On the other hand, ilT should be small enough to meet control band(cid:173)
`width and process dynamics requirements. Shannun's sampling theorem states that in a
`sampled signal, the maximum meaningful frequency is the Nyquist frequency_/;, which
`is given by half the sampling rate:
`
`I
`-
`f, = 2ilT
`
`( 1.1)
`
`It follow~ that we should select ilT such that the significant frequency content of the
`input/output signals of the particular process stay8 within the Nyquist frequency. Note
`that to be able to control the process effectively, all natural frequencies of interest in
`the plant should be smaller thanj. .. Oncefc is chosen in this manner for digital control,
`it is also important to choose analog cumponents, such as signal-conditioning device.~
`in the control system, to have an operating bandwidth larger than_{c. For example, if the
`
`Sec. 1.4
`
`Signal Classification in Control Systems
`
`7
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 14
`
`

`

`op~.:rating bandwidth of a robotic manipulator is specified to be 50 Hz, one mu~t make
`sure that the associatcu analog sensurs and tnJ.nsducers (resolvers, tachometers, etc.)
`and signakonuitioning devices (e.g., low-pass filters, charge amplifier~) have an oper(cid:173)
`ating bandwidth greater than 50 Hz-prcfcrably about 200 Hz. Furthermore, the sam(cid:173)
`pling period has to be smaller than 10m~ (from equation 1.1), preferably about 2 ms. It
`is then necessary to tnake sure that the L:ontrol computer is capable of doing all the
`processing needed in each control increment within this time. Otherwise, distribution
`of control tasks might be needed. Parallel processing is another option. Another alter(cid:173)
`native is to employ a hardware implementation of the controller. Simplification of con(cid:173)
`trol algorithms should also be attempted, but without sacrificing the accuracy require(cid:173)
`ments. In general, distributcd control is better than using a single control computer of
`larger c·apacity and faster speed.
`
`1.5 ADVANTAGES OF DIGITAL CONTROL
`
`The current trend toward using dedicated, microprocessor-based, and often decen(cid:173)
`tralized (distributed) digital control systems in industrial application.s can be rational(cid:173)
`ized in terms of the major advantages of digital control. The following are some of
`the important considerations.
`
`1. Digital control is less susceptible to noise or parameter variation in instrumen(cid:173)
`tation because data can be represented, generated, transmitted, and processed
`as binary words, with bits possessing two identifiable state.s.
`2. Very high accuracy and speed are possible through digital processing. Hard(cid:173)
`ware implementation is usually faster than software implementation.
`3. Digital control can handle repetitive tasks extremely well, through program(cid:173)
`ming.
`4. Complex control laws and signal conditioning methods that might be impracti(cid:173)
`cal to implement using 'dnalog devices can be programmed.
`5. High reliability in operation can be achieved by minimizing analog hardware
`components and through decentralization using dedicated microprocessors for
`various control tasks (see figure 1.3).
`6. Large amounts of data can be stored using compact, high-density data storage
`methods.
`7. Data can be stored or maintained for very long periods of time without drift
`and without being affected by adverse environmental conditions.
`8. Fast data transmission is possible over long distances without introducing dy(cid:173)
`namic delays, as in analog systems.
`9. Digital control has easy and iast data retrieval capabilities.
`10. Digital processing uses low operational voltages (e.g., 0-12 V DC).
`11. Digital control has low overall cost.
`
`Some of these features should be obvious; the rest should become clear as we pro(cid:173)
`ceed through the book.
`
`8
`
`Control, Instrumentation, and Design
`
`Chap. 1
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 15
`
`

`

`ust rnakc
`o:rs, etc,)
`an oper(cid:173)
`thc sam(cid:173)
`t 2 tlJS. ft
`12 ail the
`,!ribution
`her alter·
`n of con(cid:173)
`' require-
`1wuter of
`
`[1 decen(cid:173)
`rational(cid:173)
`.some of
`
`itrumen(cid:173)
`~ocessed
`g, Hard-
`
`?rogram(cid:173)
`
`i(iJpracti-
`;-·.
`
`K~tdware
`i~sors for
`h-~· ..
`r;:.: ... :
`~:§torage
`:·,:.,;'
`~x-~·.:~;·
`~out drift
`
`tW·, dy-
`Fy·:_
`
`1.6 FEEDFORWARI;> CONTROL
`
`Many control systems have inputs that do not participate in feedback controL In
`other words, these inputs are not compared with feedback (mcaslirement) signals to
`generate control s·tgnak Some of the~e inputs might be important variables in the
`plant (process) itselL Others might be undesirable inputs, .~uch as external distur(cid:173)
`bances that are unwanted yet unavoidable. Performance of a control system can gen(cid:173)
`erally be improved by measuring these (unknown) inputs and .~omehow using the in(cid:173)
`formation to generate control signals. Since the associated measurement and control
`(and compensation) take place in the forward path nf the control system, this
`method of control is known as feedforward control. Note that in feedback control,
`unknown "outputs" arc measured and compared with known (desired) inputs to gen(cid:173)
`erate control signals. In feedforward control, unknown "inputs" are measured and
`that informatinn, alnng with desired inputs, is used to generate control signals that
`can reduce error.~ due tn these unknown inputll or variations in them.
`A block diagram of a typical control loop that uses feedforward control is
`shown in figure 1.4. In this system, in addition to feedback cnntrnl, feedfnrward
`control is used to reduce the etl'ects of a disturbance input that enters the plant. The
`disturbance input is measured and fed into the controller. The controller uses this in(cid:173)
`formation tn modify the control action so as to compensate for the effect of the dis(cid:173)
`turbance input.
`
`Oistu rbance
`Input
`
`r - -
`
`Measurement
`for
`Feedforward
`
`f--
`
`!
`!
`!
`
`Reference
`Input
`
`I
`I Controller
`
`Plant
`
`I
`
`I
`I
`
`Output
`
`Measurement
`for
`Feedback
`
`Figure 1.4. A typical feedback loop that uses fccdforward control.
`
`As a practical example, consider the natural gas home heating system shown in
`tigure 1.5a. A simplified block diagram of the system is shown in figure 1.5b. In
`conventional feedback control, the room temperature is measured and its deviation
`from the desired temperature (set point) is used to adjust the natural gas How into the
`furnace. On/off control i.~ used in ~ost such applications. Even if propnrtinnal or
`three-mode (propnrtinnal- integral-derivative) control is employed, it is not easy to
`steadily mainta'm the room temperature at the desired value if there are large
`
`Sec. 1.6
`
`Feedforward Control
`
`9
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 16
`
`

`

`Vent
`
`Exhaust
`Gases
`
`t
`
`Cold Water
`In
`
`Thermal
`Insulation
`
`:~
`
`!:
`
`Actuating
`Signal
`
`Temperature Controller
`(on/off) and Tmnsm;tter
`
`(a)
`
`Unknown
`Inputs
`
`w1 ~Water F1ow Rate
`w 2 ~Temperature of Cold Water into Furnace
`w 3 =Temperature Outside the Room
`
`Temperature
`Set Point __.._
`(lnptot)
`
`Controller
`
`Furnace
`
`1----,--...-. Room Temperature
`(Output)
`
`' - - - - - - - ! Sensor- Transducer 1-*-----'
`
`(h)
`
`Figure 1.5.
`the system.
`
`(a) A natural gas home heating system. (b) A block diagram representation of
`
`changes in other (unknown) inputs to the system, such as water flow rate through the
`furnace, temperature of water entering the furnace, and outdoor temperature. Better
`results can be obtained by measuring these disturbance inputs and using that infor(cid:173)
`mation in generating the control action. This is feedforward control. Note that in the
`absence of feedforward control, any changes in the inputs w1 , w2 , and w3 in figure
`1.5 will be detected only through their effect on the feedback signal (room tempera(cid:173)
`ture). Hence, the subsequent corrective action can lag behind the cause (changes in
`w,.) considerably. This delay will lead to large errors and possible instability prob(cid:173)
`lems. With feedforward control, information on the disturbance inputs w, will be
`
`10
`
`Control, Instrumentation, and Design
`
`Chap. 1
`
`BNA/Brose Exhibit 1065
`IPR2014-00417
`Page 17
`
`

`

`.om
`iator
`
`·--~
`
`.pie
`
`Temperature
`• Set Point
`
`nperature
`fput)
`
`available to the controller immediately, thereby speeding up the control action and
`also improving the response accuntcy. Faster action and improved accuracy are two
`very desirable effects of feedforward control.
`In some applications, control inputs are computed using accurate dynamic
`models tor the plants, an