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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Model Predictive Control - Classical, Robust and Stochastic (Hardcover, 1st ed. 2016)
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Model Predictive Control - Classical, Robust and Stochastic (Hardcover, 1st ed. 2016)
Series: Advanced Textbooks in Control and Signal Processing
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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.
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