Model Predictive Control in the Process Industry - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model Predictive Control in the Process Industry write by Eduardo F. Camacho. This book was released on 2012-12-06. Model Predictive Control in the Process Industry available in PDF, EPUB and Kindle. Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.
Model Predictive Control
Model Predictive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model Predictive Control write by Eduardo F. Camacho. This book was released on 2013-01-10. Model Predictive Control available in PDF, EPUB and Kindle. The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. The book demonstrates that a powerful technique does not always require complex control algorithms. Many new exercises and examples have also been added throughout. Solutions available for download from the authors' website save the tutor time and enable the student to follow results more closely even when the tutor isn't present.
Predictive Control for Linear and Hybrid Systems
Predictive Control for Linear and Hybrid Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Predictive Control for Linear and Hybrid Systems write by Francesco Borrelli. This book was released on 2017-06-22. Predictive Control for Linear and Hybrid Systems available in PDF, EPUB and Kindle. With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).
Recent Advances in Model Predictive Control
Recent Advances in Model Predictive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Recent Advances in Model Predictive Control write by Timm Faulwasser. This book was released on 2021-04-17. Recent Advances in Model Predictive Control available in PDF, EPUB and Kindle. This book focuses on distributed and economic Model Predictive Control (MPC) with applications in different fields. MPC is one of the most successful advanced control methodologies due to the simplicity of the basic idea (measure the current state, predict and optimize the future behavior of the plant to determine an input signal, and repeat this procedure ad infinitum) and its capability to deal with constrained nonlinear multi-input multi-output systems. While the basic idea is simple, the rigorous analysis of the MPC closed loop can be quite involved. Here, distributed means that either the computation is distributed to meet real-time requirements for (very) large-scale systems or that distributed agents act autonomously while being coupled via the constraints and/or the control objective. In the latter case, communication is necessary to maintain feasibility or to recover system-wide optimal performance. The term economic refers to general control tasks and, thus, goes beyond the typically predominant control objective of set-point stabilization. Here, recently developed concepts like (strict) dissipativity of optimal control problems or turnpike properties play a crucial role. The book collects research and survey articles on recent ideas and it provides perspectives on current trends in nonlinear model predictive control. Indeed, the book is the outcome of a series of six workshops funded by the German Research Foundation (DFG) involving early-stage career scientists from different countries and from leading European industry stakeholders.
Model Predictive Control
Model Predictive Control - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Model Predictive Control write by Basil Kouvaritakis. This book was released on 2015-12-01. Model Predictive Control available in PDF, EPUB and Kindle. For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model 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; and discussion 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 find Model Predictive 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.
