I was wondering if the model predictive control toolbox supported using NARX neural networks? If not, this webpage on a neural network predictive control scheme.
I have a few questions about this:
(1). Does this Simulink-based method limit the neural network to only have a single input? Since my model is a NARX neural network with 10 exogenous inputs, I'm worried that I won't be able to use it.
(2). For the predictive control, I have only a single input that I can control which is also one of the inputs into the NARX model. I have no control over the other inputs to my NARX model (think of them as uncontrollable states in a state-space model). Due to this, I'm wondering if I can get away with optimizing the single controllable input over a prediction horizon without needing the values of the other inputs. In other words, is there a way to get multi-step output predictions for my horizon using only values from the single controllable input?
(3). Does this Simulink-based method require an actual real-time measurement of the plant output? Or is the NARX model estimation fine to use? There are no sensors to measure the plant output in real-time which is part of the reason why I designed the NARX model in the first place.
Best Answer