Abstract
The objective of this chapter is to discuss nonlinear MPC algorithms based on neural state-space models. Implementation details of the MPC-NO scheme as well as of two suboptimal MPC-NPL and MPL-NPLPT algorithms are presented. All the algorithms are considered in two versions: with the state set-point trajectory and with the output set-point trajectory. Simulation results are concerned with the polymerisation reactor introduced in the previous chapter. It is assumed that all state variables can be measured, but in practice some of them may be unavailable and an observer must be used.
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© 2014 Springer International Publishing Switzerland
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Ławryńczuk, M. (2014). MPC Algorithms Based on Neural State-Space Models. In: Computationally Efficient Model Predictive Control Algorithms. Studies in Systems, Decision and Control, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-04229-9_4
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DOI: https://doi.org/10.1007/978-3-319-04229-9_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04228-2
Online ISBN: 978-3-319-04229-9
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