Non ti piace? Non importa! Puoi restituircelo entro 30 giorni
Non puoi sbagliarti con un buono regalo. Con il buono regalo, il destinatario può scegliere qualsiasi prodotto della nostra offerta.
30 giorni per il reso
This book presents a data-driven approach to constrained control in the form of a subspace-based state-space system identification algorithm integrated into a model predictive controller. Previous research into this area focused on the system identification aspects resulting in an omission of many of the features that would make such a control strategy attractive to industry. These features include constraint handling, zero-offset set-point tracking and non-stationary disturbance rejection. Parameterisation with Laguerre orthonormal functions was proposed for the reduction in computational load of the controller. Simulation studies were performed using three real-world systems demonstrating: identification capabilities in the presence of white noise and non-stationary disturbances; unconstrained and constrained control; and the benefits and costs of parameterisation with Laguerre polynomials. The discussed algorithms have also been presented in Matlab code.
Ciao! Sono Libroamiko, il tuo consulente di libri.
Come posso aiutarti?