Model Predictive Control: Classical, Robust and Stochastic (Advanced Textbooks in Control and Signal Processing)
| Author | : | |
| Rating | : | 4.80 (516 Votes) |
| Asin | : | 3319248510 |
| Format Type | : | paperback |
| Number of Pages | : | 384 Pages |
| Publish Date | : | 2015-09-08 |
| Language | : | English |
DESCRIPTION:
just_me said The book is horrible to understand. The book is horrible to understand! The given proofs are so short and with poor explanations that basically you cannot understand anything.The book , as authors specified in preface, is a list of their study results and hence it is too farfrom being a textbook. In fact, this is not a textbook but like a coll
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. 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 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 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.. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the c
They have also been active in research, publishing hundreds of articles, in prestigious control journals. . In addition they have been Investigators and Principal Investigators in several research projects, some of which are connected with industrial partners. Both authors have lectured and tutored undergraduate students, and have supervised many final year undergraduate projects and doctoral students
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. 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. 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. 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. The book provides:extensive use of illustrative example