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Advanced Techniques and Technology of Computer-Aided Feedback Control


Advanced Techniques and Technology of Computer-Aided Feedback Control


1. Aufl.

von: Jean Mbihi

139,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 24.05.2018
ISBN/EAN: 9781119528357
Sprache: englisch
Anzahl Seiten: 256

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Beschreibungen

<p>This book covers various modern theoretical, technical, practical and technological aspects of computerized numerical control and control systems of deterministic and stochastic dynamical processes. </p>
<p>Preface xi</p> <p>Introduction xv</p> <p><b>Part 1: Advanced Elements and Test Bench of Computer-aided Feedback Control 1</b></p> <p><b>Chapter 1: Canonical Discrete State Models of Dynamic Processes 3</b></p> <p>1.1. Interest and construction of canonical state models 3</p> <p>1.2. Canonical realizations of a transfer function G(z) 4</p> <p>1.2.1. Jordan canonical realization 4</p> <p>1.2.2. Controllable canonical realization7</p> <p>1.2.3. Observable canonical realization 9</p> <p>1.3. Canonical transformations of discrete state models 11</p> <p>1.3.1. Jordan canonical transformation 12</p> <p>1.3.2. Controllable canonical transformation 13</p> <p>1.3.3. Observable canonical transformation 16</p> <p>1.3.4. Kalman canonical transformation 19</p> <p>1.4. Canonical decomposition diagram 19</p> <p>1.5. Discretization and canonical transformations using Matlab 20</p> <p>1.6. Exercises and solutions 21</p> <p><b>Chapter 2: Design and Simulation of Digital State Feedback Control Systems 27</b></p> <p>2.1. Principle of digital state feedback control 27</p> <p>2.2. Calculation of the gain K using pole placement 28</p> <p>2.3. State feedback with complete order observer 29</p> <p>2.3.1. Problem statement 29</p> <p>2.3.2. Structure of the complete or full state observer 29</p> <p>2.3.3. Synthesis diagram of the state feedback with complete observer 31</p> <p>2.4. Discrete state feedback with partial observer 34</p> <p>2.4.1. Problem statement 34</p> <p>2.4.2. Structure of the partial state observer 34</p> <p>2.4.3. Diagram of discrete state feedback with partial observer 37</p> <p>2.5. Discrete state feedback with set point tracking 40</p> <p>2.6. Block diagram of a digital control system 40</p> <p>2.7. Computer-aided simulation of a servomechanism 41</p> <p>2.7.1. Simulation of a speed servomechanism 41</p> <p>2.7.2. Computer-aided simulation of a position servomechanism 46</p> <p>2.8. Exercises and solutions 49</p> <p><b>Chapter 3: Multimedia Test Bench for Computer-aided Feedback Control  61</b></p> <p>3.1. Context and interest 61</p> <p>3.1.1. Context 61</p> <p>3.1.2. Scientific/teaching interest 62</p> <p>3.1.3. Platform presentation methodology 62</p> <p>3.2. Hardware constituents of the platform 62</p> <p>3.3. Design elements of the ServoSys software application 63</p> <p>3.3.1. Fundamental elements 63</p> <p>3.3.2. Elements of software programming 68</p> <p>3.4. Design of the ServoSys software application 74</p> <p>3.4.1. Architectural diagram of the software application 74</p> <p>3.4.2. SFC of the ServoSys multimedia platform 75</p> <p>3.5. Implementation of the ServoSys multimedia platform 80</p> <p>3.5.1. Hardware implementation 80</p> <p>3.5.2. Software implementation 81</p> <p>3.6. Overall tests of the platform 84</p> <p>3.6.1. Commissioning and procedures 84</p> <p>3.6.2. Samples of results displayed on the Matlab/GUI panel 85</p> <p>3.7. Exercises and solutions 90</p> <p><b>Part 2: Deterministic and Stochastic Optimal Digital Feedback Control 97</b></p> <p><b>Chapter 4: Deterministic Optimal Digital Feedback Control 99</b></p> <p>4.1. Optimal control: context and historical background 99</p> <p>4.1.1. Context 99</p> <p>4.1.2. Historical background 99</p> <p>4.2. General problem of discrete-time optimal control 102</p> <p>4.2.1. Principle 102</p> <p>4.2.2. Functional formulation 102</p> <p>4.3. Linear quadratic regulator (LQR) 103</p> <p>4.3.1. Definition, formulation and study methods 103</p> <p>4.3.2. H–J–B equations 104</p> <p>4.4. Translation in discrete time of continuous LQR problem 108</p> <p>4.4.1. Discretization of state equation 109</p> <p>4.4.2. Discretization of the cost function 109</p> <p>4.4.3. Case study of a scalar LQR problem 110</p> <p>4.5. Predictive optimal control 114</p> <p>4.5.1. Basic principle 114</p> <p>4.5.2. Recurrence equation of a process based on q–1 operator 116</p> <p>4.5.3. General formulation of a prediction model 116</p> <p>4.5.4. Solution and structure of predictive optimal control 118</p> <p>4.6. Exercises and solutions 119</p> <p><b>Chapter 5: Stochastic Optimal Digital Feedback Control 127</b></p> <p>5.1. Introduction to stochastic dynamic processes 127</p> <p>5.2. Stochastic LQR 128</p> <p>5.2.1. Formulation 128</p> <p>5.2.2. Resolution of the stochastic H–J–B equation 129</p> <p>5.2.3. Block diagram of stochastic LQR 133</p> <p>5.2.4. Properties of stochastic LQR 134</p> <p>5.3. Discrete Kalman filter 136</p> <p>5.3.1. Scientific context and hypotheses 136</p> <p>5.3.2. Notations 136</p> <p>5.3.3. Closed-loop algorithmic diagram 137</p> <p>5.4. Linear Quadratic Gaussian regulator 139</p> <p>5.4.1. Context 139</p> <p>5.4.2. Separation principle 140</p> <p>5.4.3. Algorithmic diagram of LQG regulator 141</p> <p>5.5. Exercises and solutions 142</p> <p><b>Chapter 6: Deployed Matlab/GUI Platform for the Design and Virtual Simulation of Stochastic Optimal Control Systems 145</b></p> <p>6.1. Introduction to OPCODE (Optimal Control Design) platform 145</p> <p>6.1.1. Scientific context 145</p> <p>6.1.2. Detailed presentation methodology 145</p> <p>6.2. Fundamental OPCODE design elements 146</p> <p>6.2.1. Elements of deterministic optimal control 146</p> <p>6.2.2. Elements of stochastic optimal control 149</p> <p>6.3. Design of OPCODE using SFC 152</p> <p>6.3.1. Architectural diagram 152</p> <p>6.3.2. Implementation of SFC 155</p> <p>6.4. Software implementation 157</p> <p>6.5. Examples of OPCODE use 159</p> <p>6.5.1. Design of deterministic optimal control systems 159</p> <p>6.5.2. Design of stochastic optimal control systems 159</p> <p>6.6. Production of deployed OPCODE.EXE application 161</p> <p>6.6.1. Interest of Matlab/GUI application deployment 161</p> <p>6.6.2. Deployment methodology 162</p> <p>6.6.3. Tests of deployed OPCODE.EXE application 162</p> <p>6.7. Exercises and solutions 164</p> <p><b>Part 3: Remotely Operated Feedback Control Systems via the Internet 169</b></p> <p><b>Chapter 7: Elements of Remotely Operated Feedback Control Systems via the Internet 171</b></p> <p>7.1. Problem statement 171</p> <p>7.2. Infrastructural topologies 172</p> <p>7.2.1. Basic topology 172</p> <p>7.2.2. Advanced topologies 173</p> <p>7.3. Remotely operated laboratories via the Internet 176</p> <p>7.3.1. Comparison between classical and remotely operated laboratories 176</p> <p>7.3.2. Infrastructures on the server side of a remotely operated laboratory 178</p> <p>7.3.3. Criteria for the creation of a remotely operated laboratory 180</p> <p>7.4. Exercises and solutions 180</p> <p><b>Chapter 8: Remotely Operated Automation Laboratory via the Internet 187</b></p> <p>8.1. Introduction to remotely operated automation laboratory 187</p> <p>8.1.1. Creation context 187</p> <p>8.1.2. Didactic context 188</p> <p>8.1.3. Specifications 188</p> <p>8.2. Design and implementation of the experimental system 189</p> <p>8.2.1. Descriptive diagrams 189</p> <p>8.2.2. Dynamic model of the real power lighting system 191</p> <p>8.2.3. Dynamic model of the PID controller for power lighting 191</p> <p>8.2.4. MMMI-aided Labview application 192</p> <p>8.3. Topology of the remotely operated automation laboratory 193</p> <p>8.3.1. Hardware infrastructure 194</p> <p>8.3.2. Specialized infrastructure on the server side 194</p> <p>8.3.3. Infrastructure on the remote operator side 196</p> <p>8.4. Use of a remotely operated laboratory via the Internet 196</p> <p>8.4.1. Procedure instruction sheet 196</p> <p>8.4.2. Samples of test results obtained with REOPAULAB 197</p> <p>8.5. Exercises and solutions 200</p> <p>Appendices 207</p> <p>Appendix 1. Table of z-transforms 209</p> <p>Appendix 2. Matlab Elements Used in this Book 211</p> <p>Appendix 3. Discretization of Transfer Functions 215</p> <p>Bibliography 217</p> <p>Index 219</p>
<strong>Jean Mbihi</strong>, University of Douala, Cameroon.

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