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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Robust and Adaptive Model Predictive Control of Nonlinear Systems (Hardcover)
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Robust and Adaptive Model Predictive Control of Nonlinear Systems (Hardcover)
Series: Control, Robotics and Sensors
Expected to ship within 18 - 22 working days
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Most physical systems possess parametric uncertainties or
unmeasurable parameters and, since parametric uncertainty may
degrade the performance of model predictive control (MPC),
mechanisms to update the unknown or uncertain parameters are
desirable in application. One possibility is to apply adaptive
extensions of MPC in which parameter estimation and control are
performed online. This book proposes such an approach, with a
design methodology for adaptive robust nonlinear MPC (NMPC) systems
in the presence of disturbances and parametric uncertainties. One
of the key concepts pursued is the concept of set-based adaptive
parameter estimation, which provides a mechanism to estimate the
unknown parameters as well as an estimate of the parameter
uncertainty set. The knowledge of non-conservative uncertain set
estimates is exploited in the design of robust adaptive NMPC
algorithms that guarantee robustness of the NMPC system to
parameter uncertainty. Topics covered include: a review of
nonlinear MPC; extensions for performance improvement; introduction
to adaptive robust MPC; computational aspects of robust adaptive
MPC; finite-time parameter estimation in adaptive control;
performance improvement in adaptive control; adaptive MPC for
constrained nonlinear systems; adaptive MPC with disturbance
attenuation; robust adaptive economic MPC; setbased estimation in
discrete-time systems; and robust adaptive MPC for discrete-time
systems.
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