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Analog Automation and Digital Feedback Control Techniques


Analog Automation and Digital Feedback Control Techniques


1. Aufl.

von: Jean Mbihi

139,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 15.03.2018
ISBN/EAN: 9781119516507
Sprache: englisch
Anzahl Seiten: 256

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Beschreibungen

<p><b>This book covers various modern theoretical, technical, practical and technological aspects of computerized numerical control and control systems of deterministic and stochastic dynamical processes.</b></p> <p>Readers will discover:</p> <ul> <li>A review of the fundamentals and results of the theory of analogue control systems</li> <li>A clear and detailed presentation on the experimental modeling of dynamic processes</li> <li>Frequency synthesis techniques and in the state space of digital control systems</li> <li>Concrete applications of deterministic and stochastic optimal regulation laws</li> <li>New multimedia platforms, training and experimental automated research</li> <li>Various topologies and creation strategies, computer-aided telecontrol regulation systems, as well as a prototype of an automated laboratory that can be remotely operated via the Internet</li> <li>Simple Matlab programs to reproduce, where necessary, the main numerical and graphical results presented</li> <li>Many exercises corrected at the end of each chapter</li> <li>Detailed studies of practical automation projects, aimed at consolidating the skills of the automation profession acquired in the book</li> </ul>
<p>Preface ix</p> <p>Introduction xiii</p> <p><b>Part 1. Analog Feedback Control Systems </b><b>1</b></p> <p><b>Chapter 1. Models of Dynamic Processes </b><b>3</b></p> <p>1.1. Introduction to dynamic processes 3</p> <p>1.1.1. Definition, hypotheses and notations 3</p> <p>1.1.2. Implications of hypotheses 4</p> <p>1.1.3. Dynamic model: an automation perspective 5</p> <p>1.2. Transfer functions 6</p> <p>1.2.1. Existence conditions 6</p> <p>1.2.2. Construction 6</p> <p>1.2.3. General structure of a transfer function 8</p> <p>1.2.4. Tools for the analysis of the properties of transfer functions 8</p> <p>1.2.5. First- and second-order transfer functions 8</p> <p>1.3. State models 12</p> <p>1.3.1. Definition 12</p> <p>1.3.2. Illustrative example 13</p> <p>1.3.3. General structure of the state model 14</p> <p>1.4. Linear state models with constant parameters 15</p> <p>1.4.1. Linearization-based construction 15</p> <p>1.4.2. Structure of a linear state model with constant parameters 16</p> <p>1.4.3. Properties of a model without pure input delay (τ<sub>0 </sub>= 0) 18</p> <p>1.5. Similarity transformation 20</p> <p>1.6. Exercises and solutions 21</p> <p><b>Chapter 2. Experimental Modeling Approach of Dynamic Processes </b><b>39</b></p> <p>2.1. Introduction to experimental modeling 39</p> <p>2.1.1. Problem statement 39</p> <p>2.1.2. Principle of experimental modeling 39</p> <p>2.1.3. Experimental modeling methodology 40</p> <p>2.2. Step response-based modeling 44</p> <p>2.2.1. Model of order 1 44</p> <p>2.2.2. Under-damped model of order 2 (ξ < 1) 44</p> <p>2.2.3. Damped model of order ≥ 2 (Strejc method) 46</p> <p>2.3. Frequency response-based modeling 50</p> <p>2.4. Modeling based on ARMA model 52</p> <p>2.4.1. ARMA model 52</p> <p>2.4.2. Parameter estimation of an ARMA model 54</p> <p>2.5. Matlab-aided experimental modeling 56</p> <p>2.6. Exercises and solutions 58</p> <p><b>Chapter 3. Review of Analog Feedback Control Systems </b><b>73</b></p> <p>3.1. Open-loop analog control 73</p> <p>3.1.1. Principle 73</p> <p>3.1.2. Open-loop control 74</p> <p>3.2. Analog control system 74</p> <p>3.3. Performances of an analog control system 75</p> <p>3.3.1. Closed-loop transfer functions 75</p> <p>3.3.2. Performance quantities 76</p> <p>3.4. Simple analog controllers 76</p> <p>3.5. PID/PIDF controllers 77</p> <p>3.5.1. Structure and role of the parameters of a PID/PIDF controller 77</p> <p>3.5.2. Ziegler–Nichols methods for parameter calculation 79</p> <p>3.5.3. Calculation of parameters by pole placement 79</p> <p>3.5.4. Direct calculation of optimal PID parameters 81</p> <p>3.5.5. LQR-based indirect calculation of optimal PID parameters 85</p> <p>3.5.6. Implementation of analog controllers 85</p> <p>3.6. Controllers described in the state space 86</p> <p>3.6.1. Principle and block diagram of a linear state feedback 86</p> <p>3.6.2. Techniques for calculating the state feedback gain 87</p> <p>3.6.3. Integral action state feedback 88</p> <p>3.6.4. State feedback with integral action and observer 90</p> <p>3.6.5. State feedback with output error compensator 92</p> <p>3.7. Principle of equivalence between PID and LQR controllers 92</p> <p>3.7.1. Proof of the equivalence principle 93</p> <p>3.7.2. Equivalence relation 96</p> <p>3.7.3. Case study 96</p> <p>3.8. Exercises and solutions 99</p> <p><b>Part 2. Synthesis and Computer-aided Simulation of Digital Feedback Control Systems </b><b>123</b></p> <p><b>Chapter 4. Synthesis of Digital Feedback Control Systems in the Frequency Domain </b><b>125</b></p> <p>4.1. Synthesis methodology 125</p> <p>4.2. Transfer function G(z) of a dynamic process 125</p> <p>4.2.1. Sampled dynamic model 125</p> <p>4.2.2. Discretization of G<sub>c</sub>(p) if input delay τ<sub>0 </sub>= 0 126</p> <p>4.2.3. Discretization of G<sub>c</sub>(s) if input delay τ<sub>0 </sub># 0 128</p> <p>4.2.4. Examples of calculation of G(z) by discretization of G<sub>c</sub>(s) 132</p> <p>4.3. Transfer function D(<i>z</i>): discretization method 136</p> <p>4.3.1. Interest of discretization 136</p> <p>4.3.2. Discretization of D<sub>c</sub>(s) by invariance methods 137</p> <p>4.3.3. Discretization of D<sub>c</sub>(s) by transformation methods 139</p> <p>4.3.4. z-Transfer functions of simple controllers 142</p> <p>4.3.5. General structure of D(z) and recurrence equation 144</p> <p>4.3.6. Discretization of transfer functions with Matlab 145</p> <p>4.4. Transfer function D(z): model method 146</p> <p>4.4.1. Principle of the model method 146</p> <p>4.4.2. Examples of direct design of digital controllers 146</p> <p>4.4.3. Conditions for the use of model approach 148</p> <p>4.4.4. Practical rules for using the model approach 149</p> <p>4.5. Discrete block diagram of digital control 150</p> <p>4.5.1. Closed-loop characteristic transfer functions 151</p> <p>4.5.2. Sampling frequency 152</p> <p>4.6. Exercises and solutions 154</p> <p><b>Chapter 5. Computer-aided Simulation of Digital Feedback Control Systems </b><b>177</b></p> <p>5.1. Approaches to computer-aided simulation 177</p> <p>5.2. Programming of joint recurrence equations 178</p> <p>5.2.1. Formulation 178</p> <p>5.2.2. Example of Matlab<sup>® </sup>programming 179</p> <p>5.3. Simulation using Matlab macro programming 183</p> <p>5.4. Graphic simulation 186</p> <p>5.5. Case study: simulation of servomechanisms 187</p> <p>5.5.1. Simulation of a speed servomechanism 187</p> <p>5.5.2. Simulation of a position servomechanism 191</p> <p>5.6. Exercises and solutions 194</p> <p><b>Chapter 6. Discrete State Models of Dynamic Processes </b><b>199</b></p> <p>6.1. Discretization of the state model of a dynamic process 199</p> <p>6.1.1. Discretization of a state model 200</p> <p>6.1.2. Discretization of a state model with input delay 201</p> <p>6.2. Calculation of {A, B, C, D} parameters of a discrete state model 204</p> <p>6.2.1. Calculation of A = e<sup>AT </sup>204</p> <p>6.2.2. Calculation of B 206</p> <p>6.2.3. Calculation of C and D 208</p> <p>6.3. Properties of a discrete state model {A, B, C, D} 208</p> <p>6.3.1. Infinity of state models of one dynamic process 208</p> <p>6.3.2. Stability 209</p> <p>6.3.3. Controllability and stabilizability 209</p> <p>6.3.4. Observability and detectability 210</p> <p>6.4. Exercises and solutions 210</p> <p>Appendices 215</p> <p>Appendix 1. Table of Z-transforms 217</p> <p>Appendix 2. Matlab<sup>®</sup> Elements Used in This Book 219</p> <p>Bibliography 223</p> <p>Index 227</p>
<p><b>Jean Mbihi,</b> University of Douala, Cameroon.</p>

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