What is Model Predictive Control (MPC)?

Model Predictive Control (MPC) is a sophisticated control strategy that uses an explicit model of a system to predict future behavior and make real-time control decisions. It works by solving an optimization problem at each time step to determine the best control actions that minimize a specified cost function while adhering to constraints. This approach allows MPC to be effective in handling multi-variable control systems and provides a flexible way to incorporate constraints, making it ideal for complex applications such as aerospace and industrial automation.

What are the key features of the book 'Model Predictive Control: Classical, Robust and Stochastic'?

This textbook presents a thorough examination of various MPC frameworks, including classical, robust, and stochastic methods. It features theoretical insights alongside practical examples to enhance understanding. Each chapter includes exercises designed to reinforce learning and ensure that readers can apply these concepts effectively in real-world situations. Furthermore, the inclusion of case studies allows practitioners and students to explore applications in diverse fields such as robotics, process control, and automotive systems.

Who is the target audience for this textbook?

The target audience for 'Model Predictive Control: Classical, Robust and Stochastic' includes graduate students, researchers, and professionals in control systems engineering and signal processing. It is particularly beneficial for those looking to deepen their understanding of advanced control techniques. Whether you are new to MPC or seek to optimize your expertise, this book serves as an essential resource for both theoretical learning and practical application in various engineering fields.

What prerequisites are needed to understand the concepts in this book?

To fully grasp the concepts presented in this textbook, readers should have a foundational understanding of control theory, linear algebra, and differential equations. Familiarity with optimization techniques will also enhance comprehension, particularly regarding the model-based decision-making process underpinning MPC. Students or professionals with a background in electrical engineering, mechanical engineering, or related disciplines will find the material more approachable and directly applicable to their work.

How does this textbook differentiate itself from other MPC resources?

This textbook stands out by integrating classical, robust, and stochastic perspectives of MPC into a single comprehensive volume. Unlike other resources that may focus on just one aspect, this book provides a holistic view that allows readers to compare and contrast different methodologies. Additionally, the use of practical examples and case studies bridges the gap between theory and application, making it a unique tool for both academics and industry practitioners interested in real-world solutions.

Can this book help with understanding non-linear MPC?

While the primary focus of this textbook is on classical, robust, and stochastic MPC methods, it provides a solid foundation that can be beneficial for readers interested in non-linear MPC. The basic principles and optimization techniques discussed within can be extended to non-linear scenarios as well. Additionally, by understanding these fundamental concepts, readers will be better equipped to explore specialized texts or research articles focused explicitly on non-linear approaches.

What applications can benefit from Model Predictive Control?

MPC is utilized across multiple industries including robotics, automotive control systems, chemical process control, and aerospace engineering. For instance, in robotics, MPC can help manage the trajectory of autonomous vehicles to navigate complex environments while adhering to dynamic constraints. In the pharmaceutical industry, it aids in ensuring optimal conditions for production processes. By leveraging its ability to handle constraints effectively, MPC is invaluable for enhancing efficiency and safety in critical applications.

What are the challenges associated with implementing MPC?

Implementing MPC can involve several challenges, such as the need for accurate system models and the computational demands required for real-time optimization. Additionally, tuning the controller to achieve desired performance can be complex and often requires considerable expertise. However, with advancements in computational power and modeling techniques, these challenges can be mitigated, making MPC a more accessible and effective control method in modern applications.

What kind of exercises are included in the book?

The book includes a variety of exercises that are designed to challenge readers and consolidate their understanding of MPC concepts. These exercises range from theoretical problems that require mathematical analysis, to practical scenarios in which readers must apply their knowledge to real-world situations. By working through these exercises, learners are encouraged to explore the implications of design choices and optimization strategies in different contexts, fostering deeper engagement with the material.

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Model Predictive Control: Classical, Robust and Stochastic (Advanced Textbooks in Control and Signal Processing)

Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems.

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Model Predictive Control: Classical, Robust and Stochastic (Advanced Textbooks in Control and Signal Processing)

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What Stands Out

Comprehensive Coverage

This textbook provides a thorough exploration of classical, robust, and stochastic model predictive control, making it an essential resource for advanced students and professionals in control engineering.

Practical Applications

It emphasizes real-world applications of model predictive control methods, ensuring readers can implement theories in practical scenarios, bridging the gap between theory and practice efficiently.

Expert Authors

Authored by leading experts in the field, this book ensures high-quality content and credible insights, making it a trusted reference for both academic and industry professionals aiming for excellence.

Product Details

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?

Control Engineers
Ideal for control engineers looking to deepen their understanding of predictive control methodologies and applications.
Advanced Students
Suitable for graduate students specializing in control systems, providing thorough explanations and examples for learning.
Researchers
Valuable for researchers focused on developing new algorithms or improving existing ones in model predictive control.

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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Model Predictive Control: Classical, Robust and Stochastic (Advanced Textbooks in Control and Signal Processing)