I have trained a neural network (NN) model in PyTorch that represents the dynamics of my system. Now, I am trying to create an L4CasADi model from the PyTorch model in order to implement model predictive control (MPC) with CasADi, following the article and : the code available on GitHub. Here is my code:

I have trained a neural network (NN) model in PyTorch that represents the dynamics of my system. Now, I am trying to create an L4CasADi model from the PyTorch model in order to implement model predictive control (MPC) with CasADi, following the article and : the code available on GitHub. Here is my code:

I have trained a neural network (NN) model in PyTorch that represents the dynamics of my system. Now, I am trying to create an L4CasADi model from the PyTorch model in order to implement model predictive control (MPC) with CasADi, following the article and : the code available on GitHub. Here is my code:

I am implementing a Model Predictive Control (MPC) to maintain the temperature of a room within a defined range while minimizing the control input. I started by training an LSTM neural network with Keras that takes a window of past data to predict the next output, but I couldn’t implement it with the existing optimization libraries. Recently, I read that MPC can be implemented with L4CasADi, but the model needs to be in PyTorch. I tried to convert my Keras model to a PyTorch model, but I couldn’t succeed, so I decided to retrain another model with PyTorch. For this, I read that the model needs to be traceable and differentiable L4CasADi. Can someone please explain this to me? Here is my model that I trained with Keras: