tuning model predictive controller
7 visualizzazioni (ultimi 30 giorni)
Mostra commenti meno recenti
Hello,
so i want to tune my Model predictive controller; the model (microgrid) is working perfectly fine with the Model predictive controller, and the results are good, but my objectives within the model (microgrid) are not totally fulfilled, I tried to adjust the weights but still,
so my question is where can i have some live lessons in order to be able tune the MPC while taking into consideration my objectives,
thanks in advance for the answer,
0 Commenti
Risposta accettata
Sam Chak
il 20 Mag 2024
Hi @Mounira
This is often a problem where designers begin with what they are trying to end with. Similar to some LQR practitioners, some choose MPC because it can autotune, hoping to achieve the performance objectives by simply specifying key parameters like the prediction horizon, control horizon, sampling time, and cost function weights, thereby avoiding the extensive mathematical intervention required for manual tuning of standard feedback controllers.
However, when performance objectives aren't met, designers often find themselves tuning more parameters than the original number of control gains in standard feedback controllers. Generally, there are no hard and fast rules in tuning, but I tend to call this the "circular tuning fallacy."
Hope these articles are helpful:
0 Commenti
Più risposte (0)
Vedere anche
Categorie
Scopri di più su Model Predictive Control Toolbox in Help Center e File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!