Command-control for Real-time Systems


A real-time system is a complex system which is an integral part of an industrial or experimental system, a vehicle or a construction machine. The peculiarity of these systems is that they are driven by real-time targets in distributed environments.
Command-control for Real-time Systems presents the calculation of correction for industrial systems of different physical natures, their implementation on real-time target industrial systems (PLC-SCADA, embedded systems with ...

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A real-time system is a complex system which is an integral part of an industrial or experimental system, a vehicle or a construction machine. The peculiarity of these systems is that they are driven by real-time targets in distributed environments.
Command-control for Real-time Systems presents the calculation of correction for industrial systems of different physical natures, their implementation on real-time target industrial systems (PLC-SCADA, embedded systems with distributed networks, Networked Control Systems) and their validation by simulation. It optimizes industrial processes by the use of automatic tools, industrial computing and communications networks and aims to successively integrate new control laws (linear, nonlinear and fuzzy controllers) so that users can leverage the power of engineering science as an automatic service process optimization while maintaining their high maintainability facilities.


1. Introduction.
2. Modeling Tools, Sébastien Cabaret and Mohammed Chadli.
3. Control Tools, Mohammed Chadli and Hervé Coppier.
4. Application to Cryogenic Systems, Marco Pezzetti, Hervé Coppier and Mohammed Chadli.
5. Applications to a Thermal System and to Gas Systems, Sébastien Cabaret and Hervé Coppier.
6. Application to Vehicles, Elie Kafrouni and Mohammed Chadli.
7. Real-time Implementation, Marco Pezzetti and Hervé Coppier.

About the Authors

Mohamed Chadli is a senior lecturer and research supervisor at the University of Picardie Jules Verne (UPJV) in France. His main research interests lie in robust control, the diagnosis and fault tolerant control of polytopic systems and applications for automobiles. He is a senior member of the IEEE, and Vice President of the AAI Club as part of SEE-France. He is the author/co-author of 3 books, book chapters and more than 100 articles published in international journals and conferences.
Hervé Coppier is a lecturing researcher at ESIEE-Amiens in France. He has collaborated with industrialists in the field of automation and industrial computing, particularly with CERN, and has spearheaded various international European projects.

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Product Details

  • ISBN-13: 9781848213654
  • Publisher: Wiley
  • Publication date: 5/13/2013
  • Series: ISTE Series, #728
  • Edition number: 1
  • Pages: 384
  • Product dimensions: 6.40 (w) x 9.30 (h) x 1.10 (d)

Table of Contents

Chapter 1. Introduction 1

Chapter 2. Modeling Tools 7
Sébastien CABARET and Mohammed CHADLI

2.1. Introduction 7

2.2. Models 9

2.2.1. Knowledge models 9

2.2.2. Behavioral models 11

2.3. The classic parametric identification methods 14

2.3.1. Graphic methods 14

2.3.2. Algorithmic methods 15

2.3.3. Validation and estimation of the model identified 19

2.4. Multi-model approach 23

2.4.1. Introduction 23

2.4.2. Techniques for obtaining multi-models 23

2.5. Bibliography 40

Chapter 3. Control Tools 43
Mohammed CHADLI and Hervé COPPIER

3.1. Linear controls 43

3.1.1. The PID corrector 43

3.1.2. The Smith predictor 44

3.1.3. Predictive functional control 49

3.1.4. Generalized predictive control 55

3.1.5. The RST controller 60

3.1.6. Implementation of the advance algorithms on a programmable logic controller: results 63

3.2. Multi-model control 82

3.2.1. Introduction 82

3.2.2. Stability analysis 83

3.2.3. State feedback control 86

3.2.4. Reconstructed state feedback control 90

3.2.5. Static output feedback control 93

3.2.6. Conclusion 97

3.3. Bibliography 98

Chapter 4. Application to Cryogenic Systems 103
Marco PEZZETTI, Hervé COPPIER and Mohammed CHADLI

4.1. Introduction 103

4.1.1. Cryogenics and its applications at CERN 103

4.1.2. Some basics about cryogenics 109

4.2. Modeling and control of a cryogenic exchanger for the NA48 calorimeter at CERN 112

4.2.1. Description of the cryogenic installations in the NA48 calorimeter 115

4.2.2. Thermal model 118

4.2.3. The TDC (Time Delay Control) corrector: application to a liquid-krypton cryogenic exchanger 120

4.3. Modeling and control of the cryogenics of the ATLAS experiment at CERN 128

4.3.1. Context and objectives of the study 128

4.3.2. Process of identification of cryogenic systems 130

4.3.3. Experimental protocol of parametric identification 136

4.3.4. Mono-variable system 142

4.3.5. Compensation for the delay with a Smith controller based on the PI corrector UNICOS 149

4.3.6. Multi-variable system 151

4.4. Conclusion 158

4.4.1. Motivations 159

4.4.2. Main contributions 160

4.5. Appendices 160

4.5.1. Appendix A 160

4.6. Bibliography 164

Chapter 5. Applications to a Thermal System and to Gas Systems  165
Sébastien CABARET and Hervé COPPIER

5.1. Advanced control of the steam temperature on exiting a superheater at a coal-burning power plant 165

5.1.1. The issue 165

5.1.2. The internal model corrector (IMC) 166

5.1.3. Multi-order regulator: 4th-order IMC 169

5.1.4. Results 171

5.2. Application to gas systems 174

5.2.1. The gas systems 174

5.2.2. The major regulations 180

5.2.3. The control system and acquisition of measurements 183

5.2.4. Modeling, identification and experimental results 184

5.3. Conclusion 202

5.4. Bibliography 202

Chapter 6. Application to Vehicles  203
Elie KAFROUNI and Mohammed CHADLI

6.1. Introduction 203

6.2. Hydraulic excavator-loader 204

6.2.1. Conventional manual piloting 205

6.3. Principle of movement of a part of the arm 206

6.3.1. Role of the drivers 206

6.3.2. Objectives 207

6.3.3. Functional specification of the interface 211

6.3.4. Limit of articular position and velocities 238

6.3.5. Articular limits 248

6.3.6. Limits of the articular velocities 259

6.3.7. 3D simulation 267

6.3.8. Onboard computer architecture 271

6.3.9. Conclusion 275

6.4. Automobiles 275

6.4.1. Models of automobiles 275

6.4.2. Validation of vehicle models 286

6.4.3. Robust control of the vehicle’s dynamics 298

6.4.4. Conclusion 318

6.5. Bibliography 319

Chapter 7. Real-time Implementation 323

7.1. Implementation of algorithms on real-time targets around distributed architectures 323

7.1.1. Introduction 323

7.1.2. Object-oriented programming in the case of a framework 324

7.1.3. MultiController 333

7.2. A distributed architecture for control (rapidity/reliability): excavator-loader testing array 347

7.2.1. Objectives of the testing array 347

7.2.2. Presentation of the onboard computer platform 348

7.2.3. Examination of the rapidity of the onboard computer structure 350

7.2.4. Results 358

7.3. Conclusion 361

7.4. Bibliography 362

General Conclusion 363

List of Authors 367

Index 369

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