This book thoroughly discusses computationally efficient
(suboptimal) Model Predictive Control (MPC) techniques based on
neural models. The subjects treated include:
. A few types of suboptimal MPC algorithms in which a linear
approximation of the model or of the predicted trajectory is
successively calculated on-line and used for prediction.
. Implementation details of the MPC algorithms for feed forward
perceptron neural models, neural Hammerstein models, neural Wiener
models and state-space neural models.
. The MPC algorithms based on neural multi-models (inspired by
the idea of predictive control).
. The MPC algorithms with neural approximation with no on-line
linearization.
. The MPC algorithms with guaranteed stability and
robustness.
. Cooperation between the MPC algorithms and set-point
optimization.
Thanks to linearization (or neural approximation), the presented
suboptimal algorithms do not require demanding on-line nonlinear
optimization. The presented simulation results demonstrate high
accuracy and computational efficiency of the algorithms. For a few
representative nonlinear benchmark processes, such as chemical
reactors and a distillation column, for which the classical MPC
algorithms based on linear models do not work properly, the
trajectories obtained in the suboptimal MPC algorithms are very
similar to those given by the ideal'' MPC algorithm with on-line
nonlinear optimization repeated at each sampling instant. At the
same time, the suboptimal MPC algorithms are significantly less
computationally demanding."
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