Digital Control Systems
Volume 2: Stochastic Control, Multivariable Control, Adaptive Control, Applications
(Sprache: Englisch)
The great advances made in large-scale integration of semiconductors and the resulting cost-effective digital processors and data storage devices determine the present development of automation. The application of digital techniques to process automation...
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Klappentext zu „Digital Control Systems “
The great advances made in large-scale integration of semiconductors and the resulting cost-effective digital processors and data storage devices determine the present development of automation. The application of digital techniques to process automation started in about 1960, when the first process computer was installed. From about 1970 process computers with cathodic ray tube display have become standard equipment for larger automation systems. Until about 1980 the annual increase of process computers was about 20 to 30%. The cost of hardware has already then shown a tendency to decrease, whereas the relative cost of user software has tended to increase. Because of the high total cost the first phase of digital process automation is characterized by the centralization of many functions in a single (though sometimes in several) process computer. Application was mainly restricted to medium and large processes. Because of the far-reaching consequences of a breakdown in the central computer parallel standby computers or parallel back-up systems had to be provided. This meant a substantial increase in cost. The tendency to overload the capacity and software problems caused further difficulties. In 1971 the first microprocessors were marketed which, together with large-scale integrated semiconductor memory units and input/output modules, can be assem bled into cost-effective microcomputers. These microcomputers differ from process computers in fewer but higher integrated modules and in the adaptability of their hardware and software to specialized, less comprehensive tasks.
Inhaltsverzeichnis zu „Digital Control Systems “
C Control Systems for Stochastic Disturbances12 Stochastic Control Systems (Introduction)
12.1 Preliminary Remarks
12.2 Mathematical Models of Stochastic Signal Processes
12.2.1 Basic Term
12.2.2 Markov Signal Processe
12.2.3 Scalar Stochastic Difference Equation
13 Parameter-optimized Controllers for Stochastic Disturbances
14 Minimum Variance Controllers for Stochastic Disturbances
14.1 Generalized Minimum Variance Controllers for Processes without Deadtime
14.2 Generalized Minimum Variance Controllers for Processes with Deadtime
14.3 Minimum Variance Controllers for Processes with Pure Deadtime
14.4 Minimum Variance Controllers without Offset
14.4.1 Additional Integral Acting Term
14.4.2 Minimization of the Control Error
14.5 Simulation Results with Minimum Variance Controllers
14.6 Comparison of Various Deterministic and Stochastic Controllers
15 State Controllers for Stochastic Disturbances
15.1 Optimal State Controllers for White Noise
15.2 Optimal State Controllers with State Estimation for White Noise
15.3 Optimal State Controllers with State Estimation for External Disturbances
D Interconnected Control Systems
16 Cascade Control Systems
17 Feedforward Control
17.1 Cancellation Feedforward Control
17.2 Parameter-optimized Feedforward Control
17.2.1 Parameter-optimized Feedforward Control without a Prescribed Initial Manipulated Variable
17.2.2 Parameter-optimized Feedforward Control with Prescribed Initial Manipulated Variable
17.2.3 Cooperation of Feedforward and Feedback Control
17.3 State Variable Feedforward Control
17.4 Minimum Variance Feedforward Control
E Multivariable Control Systems
18 Structures of Multivariable Processes
18.1 Structural Properties of Transfer Function Representations
18.1.1 Canonical Structures
18.1.2 The Characteristic Equation and Coupling Factor
18.1.3 The Influence of External Signals
18.1.4 Mutual Action of the Main Controllers
18.1.5 The Matrix Polynomial
... mehr
Representation
18.2 Structural Properties of the State Representation
19 Parameter-optimized Multivariable Control Systems
19.1 Parameter Optimization of Main Controllers without Coupling Controllers
19.1.1 Stability Regions
19.1.2 Optimization of the Controller Parameters and Tuning Rules for Twovariable Controllers
19.2 Decoupling by Coupling Controllers (Non-interaction)
19.3 Parameter Optimization of the Main and Coupling Controller
20 Multivariable Matrix Polynomial Control Systems
20.1 The General Matrix Polynomial Controller
20.2 The Matrix Polynomial Deadbeat Controller
20.3 Matrix Polynomial Minimum Variance Controllers
21 Multivariable State Control Systems
21.1 Multivariable State Control Systems
21.2 Multivariable Matrix Riccati State Controllers
21.3 Multivariable Decoupling State Controllers
21.4 Multivariable Minimum Variance State Controllers
22 State Estimation
22.1 Vector Signal Processes and Assumptions
22.2 Weighted Averaging of Two Measurements
22.3 Recursive Estimation of Vector States (Kaiman Filter)
F Adaptive Control Systems
23 Adaptive Control Systems (A Short Review)
23.1 Model Reference Adaptive Systems (MRAS)
23.1.1 Local Parameter Optimization
23.1.2 Ljapunov Design
23.1.3 Hyperstability Design
23.2 Adaptive Controllers with Identification Model (MIAS)
24 On-line Identification of Dynamical Processes and Stochastic Signals
24.1 Process and Signal Models
24.2 The Recursive Least Squares Method (RLS)
24.2.1 Dynamical Processes
24.2.2 Stochastic Signals
24.3 The Recursive Extended Least Squares Method (RELS)
24.4 The Recursive Instrumental Variables Method (RIV)
24.5 A Unified Recursive Parameter Estimation Algorithm
24.6 Modifications to Recursive Parameter Estimation Algorithms
25 On-line Identification in Closed Loop
25.1 Parameter Estimation with Perturbations
25.1.1 Indirect Process Identification
25.1.2 Direct Process Identification
25.2 Parameter Estimation with Perturbations
25.3 Methods for Closed Loop Parameter Estimation
25.3.1 Indirect Process Identification without Perturbation
25.3.2 Direct Process Identification without Perturbation
25.3.3 Direct Process Identification with Perturbation
26 Parameter-adaptive Controllers
26.1 Design Principles
26.2 Suitable Control Algorithms
26.2.1 Deadbeat Control Algorithms
26.2.2 Minimum Variance Controllers
26.2.3 Parameter-optimized Controllers
26.2.4 General Linear Controller with Pole-assignment (LCPA)
26.2.5 State Controller
26.3 Suitable Combinations
26.3.1 Ways of Combinations
26.3.2 Stability and Convergence
26.3.3 Choice of the Elements for Parameter-adaptive Controllers
26.4 Stochastic Parameter-adaptive Controllers
26.4.1 Adaptive Minimum Variance Controller (RLS/MV4)
26.4.2 Adaptive Generalized Minimum Variance Controllers (RLS/MV3, RELS/MV3)
26.5 Deterministic Parameter-adaptive Controllers
26.5.1 Adaptive Deadbeat Controller (RLS/DB)
26.5.2 Adaptive State Controller (RLS/SC)
26.5.3 Adaptive PID-Controllers
26.6 Simulation examples
26.6.1 Stochastic and Deterministic Adaptive Controllers
26.6.2 Various Processes
26.7 Start of Parameter-adaptive Controllers and Choice of Free Design Parameters
26.7.1 Preidentification
26.7.2 Choice of Design Parameters
26.7.3 Starting Methods
26.8 Supervision and Coordination of Adaptive Controllers
26.8.1 Supervision of Adaptive Controllers
26.8.2 Coordination of Adaptive Controllers
26.9 Parameter-adaptive Feedforward Control
26.10 Parameter-adaptive Multivariable Controllers
26.11 Application of Parameter-adaptive Control Algorithms
G Digital Control with Process Computers and Microcomputers
27 The Influence of Amplitude Quantization for Digital Control
27.1 Reasons for Quantization Effects
27.2 Various Quantization Effects
27.2.1 Quantization Effects of Variables
27.2.2 Quantization Effects of Coefficients
27.2.3 Quantization Effects of Intermediate Results
28 Filtering of Disturbances
28.1 Noise Sources and Noise Spectra
28.2 Analog Filtering
28.3 Digital Filtering
28.3.1 Low-pass Filters
28.3.2 High-pass Filters
28.3.3 Special Filters
29 Combining Control Algorithms and Actuators
30 Computer-aided Control Algorithm Design
30.1 Program Packages
30.1.1 Modelling through Theoretical Modelling or Identification
30.1.2 Program Packages for Process Identification
30.1.3 Program Packages for Control Algorithm Design
30.2 Case Studies
30.2.1 Digital Control of a Superheater
30.2.2 Digital Control of a Heat Exchanger
30.2.3 Digital Control of a Rotary Dryer
31 Adaptive and Selftuning Control Systems Using Microcomputers and Process Computers
31.1 Microcomputers for Adaptive Control Systems
31.2 Examples
31.2.1 Adaptive Control of a Superheater (Simulation)
31.2.2 Adaptive Control of Air Conditioning Units
31.2.3 Adaptive Control of the pH-value
- References
18.2 Structural Properties of the State Representation
19 Parameter-optimized Multivariable Control Systems
19.1 Parameter Optimization of Main Controllers without Coupling Controllers
19.1.1 Stability Regions
19.1.2 Optimization of the Controller Parameters and Tuning Rules for Twovariable Controllers
19.2 Decoupling by Coupling Controllers (Non-interaction)
19.3 Parameter Optimization of the Main and Coupling Controller
20 Multivariable Matrix Polynomial Control Systems
20.1 The General Matrix Polynomial Controller
20.2 The Matrix Polynomial Deadbeat Controller
20.3 Matrix Polynomial Minimum Variance Controllers
21 Multivariable State Control Systems
21.1 Multivariable State Control Systems
21.2 Multivariable Matrix Riccati State Controllers
21.3 Multivariable Decoupling State Controllers
21.4 Multivariable Minimum Variance State Controllers
22 State Estimation
22.1 Vector Signal Processes and Assumptions
22.2 Weighted Averaging of Two Measurements
22.3 Recursive Estimation of Vector States (Kaiman Filter)
F Adaptive Control Systems
23 Adaptive Control Systems (A Short Review)
23.1 Model Reference Adaptive Systems (MRAS)
23.1.1 Local Parameter Optimization
23.1.2 Ljapunov Design
23.1.3 Hyperstability Design
23.2 Adaptive Controllers with Identification Model (MIAS)
24 On-line Identification of Dynamical Processes and Stochastic Signals
24.1 Process and Signal Models
24.2 The Recursive Least Squares Method (RLS)
24.2.1 Dynamical Processes
24.2.2 Stochastic Signals
24.3 The Recursive Extended Least Squares Method (RELS)
24.4 The Recursive Instrumental Variables Method (RIV)
24.5 A Unified Recursive Parameter Estimation Algorithm
24.6 Modifications to Recursive Parameter Estimation Algorithms
25 On-line Identification in Closed Loop
25.1 Parameter Estimation with Perturbations
25.1.1 Indirect Process Identification
25.1.2 Direct Process Identification
25.2 Parameter Estimation with Perturbations
25.3 Methods for Closed Loop Parameter Estimation
25.3.1 Indirect Process Identification without Perturbation
25.3.2 Direct Process Identification without Perturbation
25.3.3 Direct Process Identification with Perturbation
26 Parameter-adaptive Controllers
26.1 Design Principles
26.2 Suitable Control Algorithms
26.2.1 Deadbeat Control Algorithms
26.2.2 Minimum Variance Controllers
26.2.3 Parameter-optimized Controllers
26.2.4 General Linear Controller with Pole-assignment (LCPA)
26.2.5 State Controller
26.3 Suitable Combinations
26.3.1 Ways of Combinations
26.3.2 Stability and Convergence
26.3.3 Choice of the Elements for Parameter-adaptive Controllers
26.4 Stochastic Parameter-adaptive Controllers
26.4.1 Adaptive Minimum Variance Controller (RLS/MV4)
26.4.2 Adaptive Generalized Minimum Variance Controllers (RLS/MV3, RELS/MV3)
26.5 Deterministic Parameter-adaptive Controllers
26.5.1 Adaptive Deadbeat Controller (RLS/DB)
26.5.2 Adaptive State Controller (RLS/SC)
26.5.3 Adaptive PID-Controllers
26.6 Simulation examples
26.6.1 Stochastic and Deterministic Adaptive Controllers
26.6.2 Various Processes
26.7 Start of Parameter-adaptive Controllers and Choice of Free Design Parameters
26.7.1 Preidentification
26.7.2 Choice of Design Parameters
26.7.3 Starting Methods
26.8 Supervision and Coordination of Adaptive Controllers
26.8.1 Supervision of Adaptive Controllers
26.8.2 Coordination of Adaptive Controllers
26.9 Parameter-adaptive Feedforward Control
26.10 Parameter-adaptive Multivariable Controllers
26.11 Application of Parameter-adaptive Control Algorithms
G Digital Control with Process Computers and Microcomputers
27 The Influence of Amplitude Quantization for Digital Control
27.1 Reasons for Quantization Effects
27.2 Various Quantization Effects
27.2.1 Quantization Effects of Variables
27.2.2 Quantization Effects of Coefficients
27.2.3 Quantization Effects of Intermediate Results
28 Filtering of Disturbances
28.1 Noise Sources and Noise Spectra
28.2 Analog Filtering
28.3 Digital Filtering
28.3.1 Low-pass Filters
28.3.2 High-pass Filters
28.3.3 Special Filters
29 Combining Control Algorithms and Actuators
30 Computer-aided Control Algorithm Design
30.1 Program Packages
30.1.1 Modelling through Theoretical Modelling or Identification
30.1.2 Program Packages for Process Identification
30.1.3 Program Packages for Control Algorithm Design
30.2 Case Studies
30.2.1 Digital Control of a Superheater
30.2.2 Digital Control of a Heat Exchanger
30.2.3 Digital Control of a Rotary Dryer
31 Adaptive and Selftuning Control Systems Using Microcomputers and Process Computers
31.1 Microcomputers for Adaptive Control Systems
31.2 Examples
31.2.1 Adaptive Control of a Superheater (Simulation)
31.2.2 Adaptive Control of Air Conditioning Units
31.2.3 Adaptive Control of the pH-value
- References
... weniger
Autoren-Porträt von Rolf Isermann
The two volumes of this completely revised and expanded second edition form a unit with volume 2 concentrating on stochastic control, treatment on interconnected control systems, multivariable control and adaptive digital control systems. The book is directed both towards students of electrical and mechanical engineering, computer science as well as biology, economics, mathematics, physics and towards engineers and scientists working in the field.
Bibliographische Angaben
- Autor: Rolf Isermann
- 2012, 2. Aufl., 325 Seiten, 120 Abbildungen, Maße: 24,4 cm, Kartoniert (TB), Englisch
- Verlag: Springer
- ISBN-10: 3642864228
- ISBN-13: 9783642864223
Sprache:
Englisch
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