Robust Adaptive Predictive Control
Author | : Tae-Woong Yoon |
Publisher | : |
Total Pages | : 358 |
Release | : 1994 |
Genre | : Automatic control |
ISBN | : |
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Author | : Tae-Woong Yoon |
Publisher | : |
Total Pages | : 358 |
Release | : 1994 |
Genre | : Automatic control |
ISBN | : |
Author | : Petros Ioannou |
Publisher | : Courier Corporation |
Total Pages | : 850 |
Release | : 2013-09-26 |
Genre | : Technology & Engineering |
ISBN | : 0486320723 |
Presented in a tutorial style, this comprehensive treatment unifies, simplifies, and explains most of the techniques for designing and analyzing adaptive control systems. Numerous examples clarify procedures and methods. 1995 edition.
Author | : Sunan Huang |
Publisher | : Springer Science & Business Media |
Total Pages | : 296 |
Release | : 2001-11-28 |
Genre | : Mathematics |
ISBN | : 9781852333386 |
This focused treatment includes the fundamentals and some state-of-the-art developments in the field of predictive control. A substantial part of the book addresses application issues in predictive control, providing several interesting case studies for more application-oriented readers.
Author | : Darryl DeHaan |
Publisher | : |
Total Pages | : |
Release | : 2010 |
Genre | : Technology |
ISBN | : |
Robust Adaptive Model Predictive Control of Nonlinear Systems.
Author | : Martin Guay |
Publisher | : IET |
Total Pages | : 269 |
Release | : 2015-11-13 |
Genre | : 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 | : Basil Kouvaritakis |
Publisher | : Springer |
Total Pages | : 387 |
Release | : 2015-12-01 |
Genre | : 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 | : Darryl DeHaan |
Publisher | : |
Total Pages | : |
Release | : 2010 |
Genre | : |
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 | : G.C. Goodwin |
Publisher | : Elsevier |
Total Pages | : 275 |
Release | : 2014-05-23 |
Genre | : Technology & Engineering |
ISBN | : 1483298248 |
The workshop brought together international experts in the field of robust adaptive control to present recent developments in the area. These indicated that the theory of adaptive control is moving closer to applications and is beginning to give realistic guidelines useful in practical situations. The proceedings also focused on the value of such practical features as filtering, normalization, deadzones and unification of robust control and adaptation.
Author | : Anh Tuan Le |
Publisher | : BoD – Books on Demand |
Total Pages | : 364 |
Release | : 2018-03-07 |
Genre | : Technology & Engineering |
ISBN | : 9535137964 |
This book focuses on the applications of robust and adaptive control approaches to practical systems. The proposed control systems hold two important features: (1) The system is robust with the variation in plant parameters and disturbances (2) The system adapts to parametric uncertainties even in the unknown plant structure by self-training and self-estimating the unknown factors. The various kinds of robust adaptive controls represented in this book are composed of sliding mode control, model-reference adaptive control, gain-scheduling, H-infinity, model-predictive control, fuzzy logic, neural networks, machine learning, and so on. The control objects are very abundant, from cranes, aircrafts, and wind turbines to automobile, medical and sport machines, combustion engines, and electrical machines.
Author | : Zhongsheng Hou |
Publisher | : CRC Press |
Total Pages | : 396 |
Release | : 2013-09-24 |
Genre | : Computers |
ISBN | : 1466594195 |
Model Free Adaptive Control: Theory and Applications summarizes theory and applications of model-free adaptive control (MFAC). MFAC is a novel adaptive control method for the unknown discrete-time nonlinear systems with time-varying parameters and time-varying structure, and the design and analysis of MFAC merely depend on the measured input and ou