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Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory
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Nonlinear Predictive Control Using Wiener Models - Computationally Efficient Approaches for Polynomial and Neural Structures (Hardcover, 1st ed. 2022)
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Nonlinear Predictive Control Using Wiener Models - Computationally Efficient Approaches for Polynomial and Neural Structures (Hardcover, 1st ed. 2022)
Series: Studies in Systems, Decision and Control, 389
Expected to ship within 10 - 15 working days
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This book presents computationally efficient MPC solutions. The
classical model predictive control (MPC) approach to control
dynamical systems described by the Wiener model uses an inverse
static block to cancel the influence of process nonlinearity.
Unfortunately, the model's structure is limited, and it gives poor
control quality in the case of an imperfect model and disturbances.
An alternative is to use the computationally demanding MPC scheme
with on-line nonlinear optimisation repeated at each sampling
instant. A linear approximation of the Wiener model or the
predicted trajectory is found on-line. As a result, quadratic
optimisation tasks are obtained. Furthermore, parameterisation
using Laguerre functions is possible to reduce the number of
decision variables. Simulation results for ten benchmark processes
show that the discussed MPC algorithms lead to excellent control
quality. For a neutralisation reactor and a fuel cell, essential
advantages of neural Wiener models are demonstrated.
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