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Model Predictive Control: Classical, Robust and Stochastic (Advanced Textbooks in Control and Signal Processing)
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MMK 459097
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Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems.
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- For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques.Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides:extensive use of illustrative examples;sample problems; anddiscussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage.Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will findPredictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.
| Publisher | Springer |
| Publication date | December 11, 2015 |
| Edition | 1st |
| Language | English |
| Print length | 384 pages |
| ISBN-10 | 3319248510 |
| ISBN-13 | 978-3319248516 |
| Item Weight | 2.05 pounds (930 grams) |
| Dimensions | 6.5 x 0.9 x 9.55 inches (16.5 x 2.3 x 24.3 cm) |
| Part of series | Advanced Textbooks in Control and Signal Processing |
Who Should Buy?
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Control Engineers
Ideal for control engineers looking to deepen their understanding of predictive control methodologies and applications.
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Advanced Students
Suitable for graduate students specializing in control systems, providing thorough explanations and examples for learning.
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Researchers
Valuable for researchers focused on developing new algorithms or improving existing ones in model predictive control.
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Beginner Learners
Not ideal for beginners as it assumes prior knowledge of control theory and mathematical concepts.
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Robotics & Automation Editorial Review
Model Predictive Control: Classical, Robust and Stochastic (Advanced Textbooks in Control and Signal Processing) offers a comprehensive exploration of advanced control methodologies, supported by its rigorous 384-page content published by Springer. This first edition, released on December 11, 2015, delves into key concepts essential for mastering model predictive control within various engineering applications. It provides an insightful blend of theory and practical examples, catering to those seeking a deep understanding of this sophisticated framework. Readers have praised its clear explanations and well-structured format, making it a valuable resource for both students and professionals in control engineering.
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Pros
- Comprehensive exploration of advanced control methodologies
- Rigorous content with clear explanations
- Well-structured format for easy understanding
- Ideal for students and professionals in control engineering
- Illustrates practical examples and applications
Cons
- Might require prior knowledge of control theory
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Features & Benefits
- Brings together classical predictive control with up-to-date robust and stochastic techniques
- Explains how to obtain guarantees of closed-loop stability and performance for uncertain, stochastic, constrained systems
- Discusses novel control applications such as resource allocation for sustainable development and turbine-blade control
- Use of illustrative examples and sample problems
- An invaluable guide for graduate students pursuing courses in model predictive control
- Provides an authoritative resource for the construction of courses
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