03110i0n a2200337 4500 u7329 SIRSI 20251112200005.0 |a250808s2016 gw | r 0||||00eng d 9783319248516 MiAaPQ eng rda pn MiAaPQ MiAaPQ clrauoh eng Kouvaritakis, Basil. author. Model Predictive Control : Classical, Robust and Stochastic / by Basil Kouvaritakis ; Mark Cannon. First edition: 2016. Cham : Springer International Publishing, 2016. Advanced Textbooks in Control and Signal Processing, 1439-2232 text txt rdacontent unmediated n rdamedia volume nc rdacarrier From 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. 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. Description based on publisher supplied metadata and other sources. Control engineering. System theory. Chemical engineering. Automotive engineering. Aerospace engineering. Astronautics. Cannon, Mark. author. 6346 6346 2026-03-12 00:00:00 35672004692 620 K882m 2016 35672004692 1 2025-10-14 BOOK 0 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-08-08 2026-03-12 00:00:00 35672004693 620 K882m 2016 35672004693 2 2025-10-14 BOOK 0 0 0 COL_GRAL 0 BCENTRAL BCENTRAL 2025-08-08