did-you-know? rent-now

Amazon no longer offers textbook rentals. We do!

did-you-know? rent-now

Amazon no longer offers textbook rentals. We do!

We're the #1 textbook rental company. Let us show you why.

9781852332143

Engineering Applications of Matlab 5.3 and Simulink 3

by ; ; ;
  • ISBN13:

    9781852332143

  • ISBN10:

    185233214X

  • Format: Paperback
  • Copyright: 2000-09-01
  • Publisher: Springer Verlag
  • Purchase Benefits
  • Free Shipping Icon Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • eCampus.com Logo Get Rewarded for Ordering Your Textbooks! Enroll Now
  • Write a Review Icon Write a Review
List Price: $71.95 Save up to $41.91
  • Digital
    $65.08
    Add to Cart

    DURATION
    PRICE

Supplemental Materials

What is included with this book?

Summary

Versions 5.2 and 5.3 of METLAB and version 2 and 3 or Simulink are discussed in terms of their use in tackling engineering problems. After an introduction to the most important functions of MATLAB, the functionality of the language is explained. The second part of the book is entirely dedicated to Simulink, covering applications in process control and signal processing.

Table of Contents

Analog and digital control
1(46)
The principle
1(1)
Presentation of main types of corrector
2(13)
Proportionnal corrector
2(1)
Integral control
3(1)
Derivative corrector
3(1)
Derivative return corrector
4(1)
Phase lead corrector
5(2)
Phase lag corrector
7(2)
PID controller
9(4)
Predictive action corrector
13(1)
PIR corrector, pure delay system
14(1)
Analog correctors discretisation
15(1)
Corrected systems stability
16(2)
General conditions of stability
16(1)
Nyquist criterion
17(1)
Discrete systems stability
17(1)
Examples
18(9)
Using some MATLAB® functions
18(6)
Using a PIR corrector
24(3)
LQ, LQI, quadratic linear control
27(11)
LQI control of a monovariable process
27(1)
Model without integrator
27(1)
Model with integrator
28(1)
LQI control of a multivariable process
28(1)
LQ multivariable control
29(1)
LQI multivariable control
30(1)
Application example
30(1)
LQI control of a monovariable aerothermal system
31(5)
LQI control of a multivariable system
36(2)
RST control
38(9)
Monovariable system
38(2)
Multivariable system
40(1)
Application example
40(1)
RST monovariable control of the temperature
41(2)
RST multivariable control of the aerothermal process
43(4)
State representation of continuous and discrete systems
47(48)
State representation of continuous systems
47(3)
Heuristic approach
47(2)
State representation generalization
49(1)
State representation of discrete systems
50(3)
Heuristic approach
50(2)
Application
52(1)
Controllability and observability
53(1)
Controllability
53(1)
Observability
53(1)
State reconstruction of a discrete dynamic system
54(2)
Closed-loop estimation of a deterministic process
54(2)
State return control
56(1)
Examples
57(21)
State return control system of a process including an integration
57(8)
State return control system of a process not including an integration
65(9)
Control system by poles placing of a discrete system
74(4)
Kalman filter
78(11)
Discrete stochastic Kalman predictor
89(6)
Fuzzy logic control
95(54)
The fundamental principle
95(1)
Stages of implementation of a fuzzy regulator
96(8)
Fuzzification stage
96(1)
Inference stage
97(3)
Defuzzification stage
100(4)
Graphical interface of the ``Fuzzy Logic TOOLBOX''
104(5)
Creation of a fuzzy system using the toolbox commands
109(18)
Input and output variables fuzzification
110(3)
Fuzzy Rules Editor
113(6)
Defuzzification
119(1)
Using the regulator in a control law
120(7)
Fuzzy regulator use in SIMULINK®
127(4)
Sugeno's method
131(18)
Realisation of the fuzzy regulator using the graphic interface
131(8)
Realisation of the fuzzy regulator using the TOOLBOX commands
139(10)
Neural networks
149(70)
Introduction
149(1)
Linear adaptive neural networks
150(15)
Architecture
150(1)
Training law
151(1)
Some applications fields
152(1)
Process identification
152(4)
Signal prediction
156(4)
Interference cancellation
160(5)
Neural networks with hidden layers, back-propagation error
165(8)
Principle
165(1)
Transfer functions
166(4)
Back-propagation algorithm
170(3)
Inverse neural model control
173(40)
First architecture
173(15)
Second architecture
188(10)
Addition of an integration
198(2)
Adaptive control
200(13)
Signal prediction
213(6)
Adaptive filtering
219(38)
The adaptive filtering principle
219(3)
Gradient algorithm, LMS criterion
222(1)
δ scalar adaptation choice
222(1)
Adaptation speed, filter time constant
223(1)
The recursive least squares algorithm, exact least squares criterion
223(4)
Examples of LMS adaptive filters
227(19)
Adaptive predictor for an autoregressive process
227(4)
Interference cancellation
231(8)
Extraction of a signal drowned in noise
239(7)
RLS adaptive filter example
246(11)
Extraction of a signal drowned in noise
246(11)
Application 1: Power amplifier 257(18)
Description of amplifier
257(2)
Characterization of amplifier
259(4)
Amplifier with transistors stage feedback
263(4)
Amplifier with phase lag corrector
267(5)
Amplifier with feedback of phase lead type corrector
272(3)
Application 2: Electromagnetic levitation 275(28)
Process modelling
276(3)
Expression of the F attraction power according to the I current in the coil and the e air gap
276(1)
Process linearization around a quiescent point e(t)=e0
277(1)
Process transfer functions
277(2)
Electric current amplifier control system
279(3)
Analogical and discrete models of the x(t) position control system
282(4)
x(t) digital follower control
286(5)
Using a fuzzy corrector
291(12)
Variables fuzzification
291(3)
Inference rules definition
294(1)
Output defuzzification
294(9)
Application 3: Cart with inverted pendulum 303(44)
System modelling with 2 degrees of freedom
304(3)
Kinetic energy of the system on motion
304(1)
Potential energy of the system
305(1)
Lagrange equation according to q(t)=&thetas;(t) degree of freedom
305(1)
Lagrange equation according to q(t)=x(t) degree of freedom
306(1)
Linear model around the operating point
306(1)
Linear process state modelling
307(1)
Edition and test of the discrete model
308(7)
Fuzzy regulation of the &thetas;(t) angular position
315(13)
Inputs fuzzification, membership functions definition
316(2)
Inference rules definition, defuzzification
318(4)
Achieving the fuzzy controller
322(6)
Fuzzy control of the x(t) position and the &thetas;(t) angle
328(12)
Inputs fuzzification, membership functions
329(3)
Inference rules definition, defuzzification
332(2)
Achieving the fuzzy controller
334(6)
Graphical animation of the system
340(7)
Application 4: Oven control 347(34)
Oven modelling
348(4)
Integral control with compensation of poles and zeros
352(3)
Discrete state representation of the oven
355(5)
Control by state return with integration
360(4)
Using a Kalman reconstructor
364(4)
LQ quadratic linear control
368(3)
Control by neuronal inverse model
371(10)
Application 5: Travelling gantry crane with suspended mass 381(64)
Modelling the travelling gantry crane with 2 degrees of freedom
382(2)
Kinetic energy of the system on motion
382(1)
Potential energy of the system
382(1)
Lagrange equation for the q(t)=&thetas;(t) degree of freedom
382(1)
Lagrange equation for the q(t)=x(t) degree of freedom
383(1)
Linear model upon the operating point
383(1)
Transfer functions of the system
384(5)
Step response of the open loop process
384(2)
Edition and test of the model
386(3)
Regulation of the &thetas;(t) angular position
389(2)
Regulation of the x(t) position truck and the &thetas;(t) angle
391(3)
State space modelling
394(24)
Discrete state space model
398(3)
Luenberger's state observer
401(8)
State space control of the process
409(5)
Adding an integral correction
414(4)
Graphical animation of the travelling gantry crane
418(4)
Fuzzy control of the gantry
422(7)
RST and LQI controllers
429(16)
Discrete model of the gantry
429(3)
RST control law
432(1)
RST monovariable control of the truck position
432(3)
Multivariable RST control of the travelling gantry crane
435(5)
LQI monovariable control of the cart position
440(5)
Application 6: Hands-free telephone 445(8)
Programming Adaline using MATLAB® commands
445(4)
Using S-function in a SIMULINK® model
449(4)
Application 7: Echo cancellation on a transmission line 453(10)
Transmission line modelling
454(1)
LMS filtering, 1ms1 S-function
455(5)
RLS filtering, r1s1 S-function
460(3)
Application 8: Noise elimination in a conduit 463(18)
Conduit modelling
463(1)
LMS filtering, 1ms2 S-function
464(8)
RLS filtering, r1s2 S-function
472(5)
Composite noise filtering
477(4)
Application 9: Equalisation of a symmetrical binary channel 481(22)
Generation of a random binary sequence
481(3)
The dispersion channel
484(3)
Symmetrical channel equaliser
487(9)
Use with SIMULINK®
496(7)
S-function transmission channel
497(1)
S-function LMS type adaptive equalizer
498(1)
Simulation results
499(4)
Appendix 1: S-functions under SIMULINK® 3 503(12)
1. S-functions functioning principle under SIMULINK® 3
503(1)
2. Various stages of the simulation
504(1)
3. S-function creation through a M-file call
504(8)
3. S-function creation through a C MEX file call
512(3)
Appendix 2: Masking a set of blocks in SIMULINK® 3 515(14)
1. Damped sinusoidal generator
515(8)
2. Pseudo-random binary sequence generator (PRBS)
523(6)
Bibliography 529(4)
Index 533

Supplemental Materials

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

Rewards Program