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...
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Full text (MCPHS users only) |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Hoboken :
Wiley,
2013
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| Series: | ISTE.
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| Subjects: | |
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Cover; Title Page; Contents; Chapter 1. Introduction; Chapter 2. Modeling Tools; 2.1. Introduction; 2.2. Models; 2.2.1. Knowledge models; 2.2.2. Behavioral models; 2.3. The classic parametric identification methods; 2.3.1. Graphic methods; 2.3.2. Algorithmic methods; 2.3.3. Validation and estimation of the model identified; 2.4. Multi-model approach; 2.4.1. Introduction; 2.4.2. Techniques for obtaining multi-models; Chapter 3. Control Tools; 3.1. Linear controls; 3.1.1. The PID corrector; 3.1.2. The Smith predictor; 3.1.3. Predictive functional control; 3.1.4. Generalized predictive control.
- 3.1.5. The RST controller3.1.6. Implementation of the advance algorithms on a programmable logic controller: results; 3.2. Multi-model control; 3.2.1. Introduction; 3.2.2. Stability analysis; 3.2.3. State feedback control; 3.2.4. Reconstructed state feedback control; 3.2.5. Static output feedback control; 3.2.6. Conclusion; 3.3. Bibliography; Chapter 4. Application to Cryogenic Systems; 4.1. Introduction; 4.1.1. Cryogenics and its applications at CERN; 4.1.2. Some basics about cryogenics; 4.2. Modeling and control of a cryogenic exchanger for the NA48 calorimeter at CERN.
- 4.2.1. Description of the cryogenic installations in the NA48 calorimeter4.2.2. Thermal model; 4.2.3. The TDC (Time Delay Control) corrector: application to a liquid-krypton cryogenic exchanger; 4.3. Modeling and control of the cryogenics of the ATLAS experiment at CERN; 4.3.1. Context and objectives of the study; 4.3.2. Process of identification of cryogenic systems; 4.3.3. Experimental protocol of parametric identification; 4.3.4. Mono-variable system; 4.3.5. Compensation for the delay with a Smith controller based on the PI corrector UNICOS; 4.3.6. Multi-variable system; 4.4. Conclusion.
- 4.4.1. Motivations4.4.2. Main contributions; 4.5. Appendices; 4.5.1. Appendix A; 4.6. Bibliography; Chapter 5. Applications to a Thermal System and to Gas Systems; 5.1. Advanced control of the steam temperature on exiting a superheater at a coal-burning power plant; 5.1.1. The issue; 5.1.2. The internal model corrector (IMC); 5.1.3. Multi-order regulator: 4th-order IMC; 5.1.4. Results; 5.2. Application to gas systems; 5.2.1. The gas systems; 5.2.2. The major regulations; 5.2.3. The control system and acquisition of measurements; 5.2.4. Modeling, identification and experimental results.
- 5.3. Conclusion5.4. Bibliography; Chapter 6. Application to Vehicles; 6.1. Introduction; 6.2. Hydraulic excavator-loader; 6.2.1. Conventional manual piloting; 6.3. Principle of movement of a part of the arm; 6.3.1. Role of the drivers; 6.3.2. Objectives; 6.3.3. Functional specification of the interface; 6.3.4. Limit of articular position and velocities; 6.3.5. Articular limits; 6.3.6. Limits of the articular velocities; 6.3.7. 3D simulation; 6.3.8. Onboard computer architecture; 6.3.9. Conclusion; 6.4. Automobiles; 6.4.1. Models of automobiles; 6.4.2. Validation of the vehicle models.
