Titlebook: Computationally Efficient Model Predictive Control Algorithms; A Neural Network App Maciej Ławryńczuk Book 2014 Springer International Publ

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MPC Algorithms Based on Neural State-Space Models,t 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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Cooperation between MPC Algorithms and Set-Point Optimisation Algorithms,ion. Three control structures with on-line linearisation for set-point optimisation are presented next: the multi-layer structure with steady-state target optimisation, the integrated structure and the structure with predictive optimiser and constraint supervisor. Implementation details are given for three classes of neural models.

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https://doi.org/10.1007/978-0-387-76537-2hms with neural approximation are also presented. They are very computationally efficient, because the neural approximator directly finds on-line the coefficients of the control law, successive on-line linearisation and calculations typical of the classical MPC algorithms are not necessary.

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MPC Algorithms with Neural Approximation,hms with neural approximation are also presented. They are very computationally efficient, because the neural approximator directly finds on-line the coefficients of the control law, successive on-line linearisation and calculations typical of the classical MPC algorithms are not necessary.

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