Model Predictive Control of Unstable Systems
Author : Chi Mun Cheng
Publisher :
Page : 154 pages
File Size : 35,90 MB
Release : 1987
Category :
ISBN :
Author : Chi Mun Cheng
Publisher :
Page : 154 pages
File Size : 35,90 MB
Release : 1987
Category :
ISBN :
Author : Jinfeng Liu
Publisher : MDPI
Page : 231 pages
File Size : 18,58 MB
Release : 2019-01-16
Category : Technology & Engineering
ISBN : 303897420X
This book is a printed edition of the Special Issue "New Directions on Model Predictive Control" that was published in Mathematics
Author : Basil Kouvaritakis
Publisher : Springer
Page : 387 pages
File Size : 43,29 MB
Release : 2015-12-01
Category : Technology & Engineering
ISBN : 3319248537
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.
Author : J.A. Rossiter
Publisher : CRC Press
Page : 344 pages
File Size : 42,16 MB
Release : 2003-06-27
Category : Technology & Engineering
ISBN : 0203503961
Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications. This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.
Author : Ronald Soeterboek
Publisher : Prentice Hall International
Page : 384 pages
File Size : 17,83 MB
Release : 1992
Category : Technology & Engineering
ISBN :
Describes in detail how several well-known predictive control schemes (for example, DMC and GPC) and other, more formal controller design methods can be formulated within a unified framework. The influence of the design parameters on control system performance and robustness is emphasized.
Author : James Blake Rawlings
Publisher :
Page : 770 pages
File Size : 13,95 MB
Release : 2017
Category : Control theory
ISBN : 9780975937754
Author : Liuping Wang
Publisher : Springer Science & Business Media
Page : 398 pages
File Size : 12,69 MB
Release : 2009-03-04
Category : Technology & Engineering
ISBN : 1848823304
Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: - continuous- and discrete-time MPC problems solved in similar design frameworks; - a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and - a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, coverage is given to three industrial applications. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book is mainly based on advances in MPC using state-space models and basis functions. This volume includes numerous analytical examples and problems and MATLAB® programs and exercises.
Author : Simone Loureiro de Oliveira
Publisher : vdf Hochschulverlag AG
Page : 274 pages
File Size : 19,57 MB
Release : 1996
Category : Computers
ISBN : 9783728123947
Author : Gorazd Karer
Publisher : Springer
Page : 261 pages
File Size : 21,47 MB
Release : 2012-09-20
Category : Technology & Engineering
ISBN : 3642339476
A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.
Author : Timm Faulwasser
Publisher : Springer Nature
Page : 250 pages
File Size : 31,40 MB
Release : 2021-04-17
Category : Science
ISBN : 3030632814
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.