Author : Martin Guay
Publisher :
Page : 252 pages
File Size : 34,77 MB
Release : 2015
Category : TECHNOLOGY & ENGINEERING
ISBN : 9781523101047
The following topics are dealt with: adaptive control; constrained nonlinear systems; disturbance attenuation; robust adaptive economic MPC; and discrete-time systems.
Author : Darryl DeHaan
Publisher :
Page : pages
File Size : 42,20 MB
Release : 2010
Category : Technology
ISBN :
Robust Adaptive Model Predictive Control of Nonlinear Systems.
Author : Martin Guay
Publisher : IET
Page : 269 pages
File Size : 27,64 MB
Release : 2015-11-13
Category : Technology & Engineering
ISBN : 1849195528
This book offers a novel approach to adaptive control and provides a sound theoretical background to designing robust adaptive control systems with guaranteed transient performance. It focuses on the more typical role of adaptation as a means of coping with uncertainties in the system model.
Author : Darryl DeHaan
Publisher :
Page : pages
File Size : 13,97 MB
Release : 2010
Category :
ISBN : 9789533071022
The problem of plasma vertical stabilization based on the model predictive control has been considered. It is shown that MPC algorithms are superior compared to the LQR-optimal controller, because they allow taking constraints into account and provide high-performance control. It is also shown that in the case of the traditional MPC-scheme it is possible to reduce.
Author : Brett Thomas Lopez
Publisher :
Page : 124 pages
File Size : 26,80 MB
Release : 2019
Category :
ISBN :
Modeling error and external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over control policies but at the expense of computational complexity. An alternative strategy, known as tube MPC, uses a robust controller (designed offline) to keep the system in an invariant tube centered around a desired nominal trajectory (generated online). While tube MPC regains tractability, there are several theoretical and practical problems that must be solved for it to be used in real-world scenarios. First, the decoupled trajectory and control design is inherently suboptimal, especially for systems with changing objectives or operating conditions. Second, no existing tube MPC framework is able to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. And third, the inability to reduce state-dependent uncertainty through online parameter adaptation/estimation leads to systematic error in the trajectory design. This thesis aims to address these limitations by developing a computationally tractable nonlinear tube MPC framework that is applicable to a broad class of nonlinear systems.
Author : Basil Kouvaritakis
Publisher : IET
Page : 277 pages
File Size : 12,71 MB
Release : 2001-10-26
Category : Mathematics
ISBN : 0852969848
The advantage of model predictive control is that it can take systematic account of constraints, thereby allowing processes to operate at the limits of achievable performance. Engineers in academia, industry, and government from the US and Europe explain how the linear version can be adapted and applied to the nonlinear conditions that characterize the dynamics of most real manufacturing plants. They survey theoretical and practical trends, describe some specific theories and demonstrate their practical application, derive strategies that provide appropriate assurance of closed-loop stability, and discuss practical implementation. Annotation copyrighted by Book News, Inc., Portland, OR
Author : Basil Kouvaritakis
Publisher : Springer
Page : 387 pages
File Size : 24,60 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 : Alan S.I. Zinober
Publisher : Springer Science & Business Media
Page : 396 pages
File Size : 35,74 MB
Release : 2002-09-12
Category : Technology & Engineering
ISBN : 354043240X
The objective of the EU Nonlinear Control Network Workshop was to bring together scientists who are already active in nonlinear control and young researchers working in this field. This book presents selectively invited contributions from the workshop, some describing state-of-the-art subjects that already have a status of maturity while others propose promising future directions in nonlinear control. Amongst others, following topics of nonlinear and adaptive control are included: adaptive and robust control, applications in physical systems, distributed parameter systems, disturbance attenuation, dynamic feedback, optimal control, sliding mode control, and tracking and motion planning.
Author : Eduardo F. Camacho
Publisher : Springer Science & Business Media
Page : 250 pages
File Size : 45,98 MB
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 1447130081
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.
Author : Lars Grüne
Publisher : Springer
Page : 463 pages
File Size : 33,54 MB
Release : 2016-11-09
Category : Technology & Engineering
ISBN : 3319460242
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.
