Digital Control Systems: Design, Identification and Implementation (Communications and Control Engineering)

✍ Scribed by Ioan Doré Landau

• Publisher: Springer
• Year: 2006
• Tongue: English
• Leaves: 497
• Category: Library

✦ Synopsis

The extraordinary development of digital computers (microprocessors, microcontrollers) and their extensive use in control systems in all fields of applications has brought about important changes in the design of control systems. Their performance and their low cost make them suitable for use in control systems of various kinds which demand far better capabilities and performances than those provided by analog controllers. However, in order really to take advantage of the capabilities of microprocessors, it is not enough to reproduce the behavior of analog (PID) controllers. One needs to implement specific and high-performance model based control techniques developed for computer-controlled systems (techniques that have been extensively tested in practice). In this context identification of a plant dynamic model from data is a fundamental step in the design of the control system. The book takes into account the fact that the association of books with software and on-line material is radically changing the teaching methods of the control discipline. Despite its interactive character, computer-aided control design software requires the understanding of a number of concepts in order to be used efficiently. The use of software for illustrating the various concepts and algorithms helps understanding and rapidly gives a feeling of the various phenomena.

✦ Table of Contents

i-iv.pdf
v-viii_Dedication.pdf
ix-xiv_Preface.pdf
Preface
xv-xxii_Contents.pdf
Contents
xxiii-xxiv_Principal_Notation.pdf
List of Principal Notation
Warning
1-24.pdf
25-84.pdf
Computer Control Systems
2.1 Introduction to Computer Control
2.2 Discretization and Overview of Sampled-data Systems
2.2.1 Discretization and Choice of Sampling Frequency
2.2.2 Choice of the Sampling Frequency for Control Systems
2.3 Discrete-time Models
2.3.1 Time Domain
2.3.2 Frequency Domain
2.3.3 General Forms of Linear Discrete-time Models
2.3.4 Stability of Discrete-time Systems
2.3.5 Steady-state Gain
2.3.6 Models for Sampled-data Systems with Hold
2.3.7 Analysis of First-order Systems with Time Delay
2.3.8 Analysis of Second-order Systems
2.4 Closed Loop Discrete-time Systems
2.4.1 Closed Loop System Transfer Function
2.4.2 Steady-state Error
2.4.3 Rejection of Disturbances
2.5 Basic Principles of Modern Methods for Design of Digital
2.5.1 Structure of Digital Controllers
2.5.2 Digital Controller Canonical Structure
2.5.3 Control System with PI Digital Controller
2.6 Analysis of the Closed Loop Sampled-Data Systems in the
2.6.1 Closed Loop Systems Stability
2.6.2 Closed Loop System Robustness
2.7 Concluding Remarks
2.8 Notes and References
85-168.pdf
Robust Digital Controller Design Methods
3.1 Introduction
3.2 Digital PID Controller
3.2.1 Structure of the Digital PID 1 Controller
3.2.2 Design of the Digital PID 1 Controller
3.2.3 Digital PID 1 Controller: Examples
3.2.4 Digital PID 2 Controller
3.2.5 Effect of Auxiliary Poles
3.2.6 Digital PID Controller: Conclusions
3.3 Pole Placement
3.3.1 Structure
3.3.2 Choice of the Closed Loop Poles (P(q-1))
3.3.3 Regulation (Computation of R(q-1) and S(q-1))
3.3.4 Tracking (Computation of T(q-1))
3.3.5 Pole Placement: Examples
3.4 Tracking and Regulation with Independent Objectives
3.4.1 Structure
3.4.2 Regulation (Computation of R(q-1) and S(q-1))
3.4.3 Tracking (Computation of T(q-1))
3.4.4 Tracking and Regulation with Independent Objectives: E
3.5 Internal Model Control (Tracking and Regulation)
3.5.1 Regulation
3.5.2 Tracking
3.5.3 An Interpretation of the Internal Model Control
3.5.4 The Sensitivity Functions
3.5.5 Partial Internal Model Control (Tracking and Regulatio
3.5.6 Internal Model Control for Plant Models with Stable Ze
3.5.7 Example: Control of Systems with Time Delay
3.6 Pole Placement with Sensitivity Function Shaping
3.6.1 Properties of the Output Sensitivity Function
3.6.2 Properties of the Input Sensitivity Function
3.6.3 Definition of the “Templates” for the Sensitivity Func
3.6.4 Shaping of the Sensitivity Functions
3.6.5 Shaping of the Sensitivity Functions: Example 1
3.6.6 Shaping of the Sensitivity Functions: Example 2
3.7 Concluding Remarks
3.8 Notes and References
169-200.pdf
Design of Digital Controllers in the Presence of Random Dist
4.1. Models for Random Disturbances
4.1.1 Description of the Disturbances
4.1.2 Models of Random Disturbances
4.1.3 The ARMAX Model (Plant + Disturbance)
4.1.4 Optimal Prediction
4.2. Minimum Variance Tracking and Regulation
4.2.1 An Example
4.2.2 General Case
4.2.3 Minimum Variance Tracking and Regulation: Example
4.3 The Case of Unstable Zeros: Approximation of the Minimum
4.3.1 Controller Design
4.3.2 An Example
4.4 Generalized Minimum Variance Tracking and Regulation
4.4.1 Controller Design
4.5 Concluding Remarks
4.6 Notes and References
201-246.pdf
System Identification: The Bases
5.1. System Model Identification Principles
5.2 Algorithms for Parameter Estimation
5.2.1 Introduction
5.2.2 Gradient Algorithm
5.2.3 Least Squares Algorithm
5.2.4 Choice of the Adaptation Gain
5.3 Choice of the Input Sequence for System Identification
5.3.1 The Problem
5.3.2. Pseudo-Random Binary Sequences (PRBS)
5.4 Effects of Random Disturbances upon Parameter Estimation
5.5 Structure of Recursive Identification Methods
5.6 Concluding Remarks
5.7 Notes and References
247-278.pdf
System Identification Methods
6.1 Identification Methods Based on the Whitening of the Pre
6.1.1 Recursive Least Squares (RLS)
6.1.2 Extended Least Squares (ELS)
6.1.3 Recursive Maximum Likelihood (RML)
6.1.4 Output Error with Extended Prediction Model (OEEPM)
6.1.5 Generalized Least Squares (GLS)
6.2 Validation of the Models Identified with Type I Methods
6.3 Identification Methods Based on the Uncorrelation of the
6.3.1 Instrumental Variable with Auxiliary Model
6.3.2 Output Error with Fixed Compensator
6.3.3 Output Error with (Adaptive) Filtered Observations
6.4. Validation of the models identified with Type II Method
6.5. Estimation of the Model Complexity
6.5.1 An Example
6.5.2 The Ideal Case (No Noise)
6.5.3 The Noisy Case
6.5.4 Criterion for Complexity Estimation
6.6 Concluding Remarks
6.7 Notes and References
279-316.pdf
Practical Aspects of System Identification
7.1 Input/Output Data Acquisition
7.1.1 Acquisition Protocol
7.1.2 Anti-Aliasing Filtering
7.1.3 Over Sampling
7.2 Signal Conditioning
7.2.1 Elimination of the DC Component
7.2.2 Identification of a Plant Containing a Pure Integrator
7.2.3 Identification of a Plant Containing a Pure Differenti
7.2.4 Scaling of the Inputs and Outputs
7.3 Selection (Estimation) of the Model Complexity
7.4 Identification of Simulated Models: Examples
7.5 Plant Identification Examples
7.5.1 Air Heater
7.5.2 Distillation Column
7.5.3 DC Motor
7.5.4 Flexible Transmission
7.6 Concluding Remarks
7.7 Notes and References
317-374.pdf
Practical Aspects of Digital Control
8.1 Implementation of Digital Controllers
8.1.1 Choice of the Desired Performances
8.1.3 Effect of the Digital-to-analog Conversion
8.1.4 Effect of the Saturation: Anti Windup Device
8.1.5 Bumpless Transfer from Open Loop to Closed Loop Operat
8.1.6 Digital Cascade Control
8.1.7 Hardware for Controller Implementation
8.1.8 Measuring the Quality of a Control Loop
8.1.9 Adaptive Control
8.2 Digital Control of an Air Heater
8.3 DC Motor Speed Control
8.4 Cascade Position Control of a DC Motor Axis
8.5 Position Control by means of a Flexible Transmission
8.6 Control of a 360° Flexible Robot Arm
8.7 Control of Deposited Zinc in Hot Dip Galvanizing (Sollac
8.7.1 Description of the Process
8.7.2 Process Model
8.7.3 Model Identification
8.7.4 Controller Design
8.7.5 Open Loop Adaptation
8.7.6 Results
8.8 Concluding Remarks
8.9 Notes and References
375-398.pdf
Identification in Closed Loop
9.1 Introduction
9.2 Closed Loop Output Error Identification Methods
9.2.1 The Principle
9.2.2 The CLOE, F-CLOE and AF-CLOE Methods
9.2.3 Extended Closed Loop Output Error (X-CLOE)
9.2.4 Identification in Closed Loop of Systems Containing an
9.2.5 Model Validation in Closed Loop
9.3 Other Methods for Identification in Closed Loop
9.4 Identification in Closed Loop: A Simulated Example
9.5 Identification in Closed Loop and Controller Re-Design (
9.6 Concluding Remarks
9.7 Notes and References
399-416.pdf
Reduction of Controller Complexity
10.1 Introduction
10.2 Estimation of Reduced Order Controllers by Identificati
10.2.1 Closed Loop Input Matching (CLIM)
10.2.2 Closed Loop Output Matching (CLOM)
10.2.3 Taking into Account the Fixed Parts of the Nominal Co
10.2.4 Re-Design of Polynomial T(q-1)
10.3 Validation of Reduced Order Controllers
10.3.1 The Case of Simulated Data
10.3.2 The Case of Real Data
10.4 Practical Aspects
10.5 Control of a Flexible Transmission – Reduction of Contr
10.6 Concluding Remarks
10.7 Notes and References
417-422_A.pdf
A Brief Review of Some Results from Theory of Signals and Pr
A.1 Some Fundamental Signals
A.2 The - transform
A.3 The Gauss Bell
423-440_B.pdf
Design of RST Digital Controllers in the Time Domain
B.1 Introduction
B.2 Predictors for Discrete Time Systems
B.3 One Step Ahead Model Predictive Control
B.4 An Interpretation of the Control of Systems with Delay
B.5 Long Range Model Predictive Control
B.6 Notes and References
441-450_C.pdf
State-Space Approach for the Design of RST Controllers
C.1 State-Space Design
C.2 Linear Quadratic Control
451-456_D.pdf
Generalized Stability Margin and Normalized Distance Between
D.1 Generalized Stability Margin
D.2 Normalized Distance Between Two Transfer Functions
D.3 Robust Stability Condition
D.4 Notes and References
457-460_E.pdf
The Youla–Kučera Controller Parametrization
E.1 Controller Parametrization
E.2 Notes and References
461-462_F.pdf
The Adaptation Gain Updating – The U–D Factorization
F.1 The U-D Factorization
F.2 Notes and References
463-470_G.pdf
Laboratory Sessions
G.1 Sampled-data Systems
G.2 Digital PID Controller
G.3 System Identification
G.4 Digital Control (Distillation Column Control Design)
G.5 Closed Loop Identification
G.6 Controller Reduction
471-472_H.pdf
List of Functions (MATLAB®, Scilab and C++)
Scilab Function
MATLAB® Function
C++ function
Description
473-478_References.pdf
References
479-484_Index.pdf