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  • Produktbild: Digital Control Systems
  • Produktbild: Digital Control Systems
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Digital Control Systems Volume 2: Stochastic Control, Multivariable Control, Adaptive Control, Applications

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77,99 € UVP 87,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

25.11.2012

Verlag

Springer Berlin

Seitenzahl

325

Maße (L/B/H)

24,4/17/2 cm

Gewicht

607 g

Auflage

Second Edition 1991

Sprache

Englisch

ISBN

978-3-642-86422-3

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

25.11.2012

Verlag

Springer Berlin

Seitenzahl

325

Maße (L/B/H)

24,4/17/2 cm

Gewicht

607 g

Auflage

Second Edition 1991

Sprache

Englisch

ISBN

978-3-642-86422-3

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

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  • Produktbild: Digital Control Systems
  • Produktbild: Digital Control Systems
  • C Control Systems for Stochastic Disturbances.- 12 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 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.