000			03110i0n a2200337 4500
001			u7329
003			SIRSI
005			20251112200005.0
008			|a250808s2016 gw | r 0||||00eng d
020			_a9783319248516
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ _dclrauoh
041			_aeng
100			_aKouvaritakis, Basil. _eauthor.
245	1	0	_aModel Predictive Control : _bClassical, Robust and Stochastic / _cby Basil Kouvaritakis ; Mark Cannon.
250			_aFirst edition: 2016.
264			_aCham : _bSpringer International Publishing, _c2016.
440			_aAdvanced Textbooks in Control and Signal Processing, _x1439-2232
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aFrom the Contents: Introduction -- Classical Model Predictive Control -- Robust Model Predictive Control with Additive Uncertainty: Open-loop Optimization Strategies -- Robust Model Predictive Control with Additive Uncertainty: Closed-loop Optimization Strategies.
520			_aFor 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.
588			_aDescription based on publisher supplied metadata and other sources.
650			_aControl engineering.
650			_aSystem theory.
650			_aChemical engineering.
650			_aAutomotive engineering.
650			_aAerospace engineering.
650			_aAstronautics.
700			_aCannon, Mark. _eauthor.
999			_c6346 _d6346