Table of contents for Practical control engineering : guide for engineers, managers, and practitioners / David M. Koenig.

Bibliographic record and links to related information available from the Library of Congress catalog.

Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.

begining of table of contents
Chapter One: Qualitative Concepts in Control Engineering and Process Analysis
1.1 What is a Feedback Control? 
1.2 What is a FeedForward Controller?
1.3 Process Disturbances
1.4 Comparing Feedback and FeedForward Controllers
1.5 Combining Feedback and FeedForward Controllers
1.6 Why is Feedback Control Difficult to Carry Out?
1.7 An Example of Controlling a Noisy Industrial Process
1.8 What is a Control Engineer?
1.9 Summary and Conclusions 
Chapter Two: Introduction to Developing Control Algorithms
2.1 Approaches to Developing Control Algorithms
	2.1.1 Style, Massive Intelligence, Luck and Heroism (SMILH)
	2.1.2 A Priori First Principles
	2.1.3 A Common Sense, Pedestrian Approach
2.2 Dealing with the Existing Process
	2.2.1 What is the Problem?
	2.2.2 The Diamond Road Map
		Compartmentalization and Requirements Gathering
		Where to Start?
		Massive Cross Correlation
		Time Domain Analysis
		Frequency Domain Analysis
		Step Change Response Analysis
		Control Development
2.3 Dealing with Control Algorithms Bundled with the Process
	What is the Problem?
	Separation and Success
	Problem Solving and Bundling
2.4 Some Comments on Debugging Control Algorithms
	Rookie Fright
	When in Doubt, Simulate ¿ Not!
	At Last ¿ Busted!
	Surprise Sub
	Totally Covering my Derriere
	It¿s Too Complicated ¿ Use the Process for Debugging
2.5 Documentation and Indispensability
2.6 Summary and Conclusions
Chapter Three: Basic Concepts in Process Dynamics
 3.1 The First Order Process ¿ An Introduction
 The Process Gain and Time Constant
3.2 Mathematical Descriptions of the First Order Process
 3.2.1 The Continuous-Time Domain Model
 3.2.2 Solution of the Continuous-Time Domain Model
 	Comments about the Solution
 3.2.3 The First Order Model and Proportional Control
 	Faster Response
 	Offset from Set Point
 3.2.4 The First Order Model and Proportional-Integral Control
 Showing that there is no Offset
		Trying a Partial Solution for the Transient Part
		Critical Damping
 Overdamped Response
 So What?
3.3 The Laplace Transform
 3.3.1 The Transfer Function and Block Diagram Algebra
 3.3.2 Applying the New Tool to the First Order Model
 3.3.4 The Laplace Transform of Derivatives
3.3.5 Applying the Laplace Transform to the Case with Proportional plus Integral Control
 3.3.6 More Block Diagram Algebra and Some Useful Transfer Functions
 3.3.7 Zeros and Poles 
 Partial Fractions and Poles
 Poles and Time Domain Exponential Terms
3.4 Review and Summary
Chapter Four: A New Domain and More Process Models
4.1 Onward to the Frequency Domain
 Sinusodially Disturbing the First Order Process
 A Little Mathematical Support in the Time Domain
 A Little Mathematical Support in the Laplace Transform Domain
	A Little Graphical Support
 A Graphing Trick
4.2 How Can Sinusoids Help Us with Understanding Feedback Control?
4.3 The First Order Process with Feedback Control in the Frequency Domain
 What¿s this about the Integral?
 What about adding P to the I?
Partial Summary and a Rule of Thumb using Phase Margin and Gain 
4.4 A Pure Deadtime Process
	Proportional-Only Control of a Pure Deadtime Process
	Integral-Only Control of a Pure Deadtime Process
4.5 A First Order Process with Deadtime (FOWDT) Process
	The Concept of Minimum Phase
	Proportional-Only Control
	Proportional-Integral Control of the FOWDT Process
4.6 A Few Comments about Simulating Processes with Variable Deadtimes
4.7 Partial Summary and a Slight modification of the Rule of Thumb
4.8 Summary and Conclusions
Chapter Five. Matrices and Higher Order Process Models
5.1 Third Order Processes without Back Flow
	The Laplace Transform Version
	The Frequency Domain Version
	The Matrix (State Space) Version
5.2 Third Order Process with Back Flow
	The State Space Version
5.3 Control of Three Tank System with No Back Flow
	Closed Loop Performance in the Frequency Domain
5.4 Critical Values and Finding the Poles
5.5 Multi-Tank Processes
	Matching the N-Tank Model with a FOWDT Model
5.6 Summary and Conclusions
Chapter Six: An Underdamped Process
6.1 The Dynamics of the Mass/Spring/Dashpot Process 
6.2 Solutions in Four Domains
	Time Domain
	Laplace Domain Solution
	Frequency Domain
	State Space Representation
	Scaling and Round-Off Error
6.3 PI Control of the Mass/Spring/Dashpot Process
6.4 Derivative Control (PID)
	Complete Cancellation
	Adding Sensor Noise
	Filtering the Derivative
6.5 Compensation Before Control-The Transfer Function Approach
6.6 Compensation Before Control-The State Space Approach
6.7 An Electrical Analog to the Mass-Dashpot-Spring Process
6.8. Summary and Conclusions
Chapter Seven: Distributed Processes
7.1 The Tubular Energy Exchanger ¿ Steady State
7.2 The Tubular Energy Exchanger ¿ Transient Behavior
	Transfer by Diffusion
7.3 Solution of the Tubular Heat Exchanger Equation
 Inlet Temperature Transfer Function
 Steam Jacket Temperature Transfer Function
7.4 Response of Tubular Heat Exchanger to Step in Jacket Temperature
 The Large Diameter Case
 The Small Diameter Case
7.5 Studying the Tubular Energy Exchanger in the Frequency Domain.
7.6 Control of the Tubular Energy Exchanger
7.7 Lumping the Tubular Energy Exchanger
 Modeling an Individual Lump
 Steady State Solution
 Discretizing the Partial Differential Equation
7.8 Lumping and Axial Transport
7.9 State Space Version of the Lumped Tubular Exchanger
7.10 Summary and Review
Chapter 8: Stochastic Process Disturbances and the Discrete Time Domain
8.1 The Discrete Time Domain
8.2 White Noise and Sample Estimates of Population Measures
The Sample Average
The Sample Variance
The Histogram
The Sample Autocorrelation
The Line Spectrum
The Cumulative Line Spectrum
8.3 Non-White Stochastic Sequences
	Positively Autoregressive Sequences
	Negatively Autoregressive Sequences
Moving Average Stochastic Sequences
Unstable Nonstationary Stochastic Sequences
Multi-Dimensional Stochastic Processes and the Covariance
8.4 Populations, Realizations, Samples, Estimates and Expected Values
	Expected Value
	Ergodicity and Stationarity
	Applying the Expectation Operator
8.5 Comments on Stochastic Disturbances and Difficulty of Control
White Noise
Colored Noise
8.6 Summary and Conclusions
Chapter Nine: The Discrete Time Domain and the Z-Transform
9.1 Discretizing the First Order Model
9.2 Moving to the Z-Domain via the Back Shift Operator
9.3 Sampling and Zero-Holding
9.4 Recognizing the First Order Model as a Discrete-Time Filter
9.5 Discretizing the FOWDT Model
9.6 The PI Control Equation in the Discrete Time Domain
9.7 Converting the PI Control Algorithm to Z-Transforms
9.8 The PIfD Control Equation in the Discrete Time Domain
9.9 Using the Laplace Transform to Design Control Algorithms ¿ The Q Method
 Developing the PI Control Algorithm
 Developing a PID-Like Control Algorithm
9.10 Using the Z-Transform to Design Control Algorithms
9.11 Designing a Control Algorithm for a Dead-Time process
9.12 Moving to the Frequency Domain
The First Order Process Model
The Ripple
 Sampling and Replication
9.13 Filters
 Autogressive Filters
Moving Average Filters
A Double Pass Filter
High Pass Filters
9.14 Frequency Domain Filtering
9.15 The Discrete-Time State Space Equation
9.16 Determining Model Parameters from Experimental Data
First Order Models
Third Order Models
A Practical Method
9.17 Process Identification with White Noise Inputs
9.18 Summary
Chapter Ten: Estimating the State and Using It for Control
10.1 An Elementary Presentation of the Kalman Filter
The Process Model
The Pre-Measurement and Post-Measurement Equations
The Scalar Case
A Two-Dimensional Example
The Propagation of the Covariances
The Kalman Filter Gain
10.2 Estimating the Underdamped Process State
10.3 The Dynamics of the Kalman Filter and an Alternative Way to Find the Gain
 The Dynamics of a Predictor Estimator
10.4 Using the Kalman Filter for Control
 A Little Detour to Find the Integral Gain
10.5 Feeding Back the State for Control
	Integral Control?
10.6 Integral and Multi-Dimensional Control
 Setting up the Example Process and Posing the Control Problem
 Developing the Discrete Time Version
Finding the Open Loop Eigenvalues and Placing the Closed Loop Eigenvalues
 Implementing the Control Algorithm
10.7 PI Control Applied to the Three Tank Process
10.8 Control of the Lumped Tubular Energy Exchanger
10.9 Miscellaneous Issues
Optimal Control
Continuous-Time Domain Kalman Filter
10.10 Summary
Chapter Eleven: A Review of Control Algorithms
11.1 The Strange Motel Shower Stall Control Problem
11.2 Identifying the Strange Motel Shower Stall Control Approach as Integral-Only
11.3 Proportional-Integral, Proportional-Only, and PID Control
PI Control
P-Only Control
PID Control
Modified PID Control
11.4 Cascade Control
11.5 Control of White Noise ¿ Conventional Feedback Control vs. SPC
11.6 Control Choices
11.7 Analysis and Design Tool Choices
Appendix A: Rudimentary Calculus
The Automobile Trip
The Integral, Area and Distance
Approximation of the Integral 
Integrals of Useful Functions
The Derivative, Rate of Change and Acceleration
Derivatives of Some Useful Functions
The Relation between the Derivative and the Integral 
Some Simple Rules of Differentiation
A Useful Test Function
Appendix B: Complex Numbers
	Complex Conjugates
 Complex Numbers as Vectors or Phasors
 Euler¿s Equation
 An Application to a Problem in Chapter Four
 The Full Monty
Appendix C: Spectral Analysis
An Elementary Discussion of the Fourier Transform as a Data Fitting 
Partial Summary
	Dectecting Periodic Components
	The Line Spectrum
The Exponential Form of the Least Squares Fitting Equation
	Periodicity in the Time Domain
Sampling and Replication
	Apparent Increased Frequency-Domain Resolution via Padding
The Variance and the Discrete Fourier Transform
Impact of Increased Frequency Resolution on Variability of the Power 
Appendix D. Infinite and Taylor¿s Series
Appendix E. Application of the Exponential Function to Differential Equations
 First Order Differential Equations
 Partial Summary
 Partial Solution of a Second Order Differential Equation
Appendix F. The Laplace Transform
Laplace Transform of a Constant (or a Step Change)
Laplace Transform of a Step at a Time Greater than Zero
Laplace Transform of a Delayed Quantity
Laplace transform of the Impulse or Dirac Delta function
Laplace Transform of the Exponential Function
Laplace Transform of a Sinusoid
Final Value Theorem
Laplace Transform Tables
Laplace Transform of the Time Domain Derivative
Laplace Transform of Higher Derivatives
Laplace Transform of an Integral
The Laplace Transform Recipe
Applying the Laplace Transform to the First Order Model: The Transfer
Applying the Laplace Transform to the First Order Model: The Impulse
Applying the Laplace Transform to the First Order Model: The Step
Partial Fraction Expansions Applied to Laplace Transforms: The First
 Order Problem
Partial Fraction Expansions Applied to Laplace Transforms: The Second
 Order Problem
A Precursor to the Convolution Theorem
Using the Integrating Factor to Obtain the Convolution Integral
Application of the Laplace Transform to a First Order Partial Differential
Solving the Transformed Partial Differential Equation
The Magnitude and Phase of the Transformed Partial Differential Equation
A Brief History of the Laplace Transform
Appendix G. Vectors and Matrices
Addition and Multiplication of Matrices
State Space Equations and Laplace Transforms
Transposes and Diagonal Matrices
Determinants, Cofactors and Adjoints of a Matrix
The Inverse Matrix
Some Matrix Calculus
The Matrix Exponential Function and Infinite Series
Eigenvalues of Matrices
Eigenvalues of Transposes
More on Operators
The Cayley-Hamilton Theorem
Appendix H. Solving the State Space Equation
Solving the State Space Equation in the Time Domain for a Constant Input
Solution of the State Space Equation using the Integrating Factor
Solving the State Space Equation in the Laplace Transform Domain
The Discrete-Time State Space Equation
Appendix I. The Z-Transform
The Sampling Process and the Laplace Transform of a Sampler
The Zero-Order Hold
Z-Transform of the Constant (Step Change)
Z-Transform of the Exponential Function
The Kronecker Delta and its Z-Transform
Some Complex Algebra and the Unit Circle in the z-Plane
A Partial Summary
Developing Z-Transform Transfer Functions from Laplace Tranforms with
Poles and Associated Time Domain Terms
Final Value Theorem
Appendix J: A Brief Exposure to Matlab

Library of Congress Subject Headings for this publication:

Automatic control.
Control theory.