Learn prediction models from data
Model predictive control (MPC) requires a mathematical representation of the dynamics of the process to compute predictions. Physics-based nonlinear models are sometimes too complex to develop and maintain, and may lead to difficult nonlinear programming problems to solve online when evaluating the MPC controller. In alternative, data-driven models can be used to directly learn the prediction model (or parts of it) from experimental data.
ODYS Deep Learning Toolset is a software library, available in MATLAB® and in Python, conceived to help the development of black-box models from data for use with ODYS Embedded MPC. It offers the following main features:
- Interfaces with most common machine-learning libraries in Python (keras/tensorflow, sklearn, pytorch).
- Very efficient proprietary batch algorithms based on regularized nonlinear least-squares for learning deep neural networks, using most common activation functions, arbitrary differentiable output functions, and arbitrary differentiable loss functions, and recursive algorithms for online learning and adaptive NL-MPC. Compared to most common deep-learning libraries, our algorithms are particularly efficient in learning small-scale neural networks, which are typically used in MPC, in terms of quality of fit and computation time.
- Automatic generation of optimized C code of neural network models and their Jacobian matrices for nonlinear MPC setup. The generated code is ready to be use as prediction model in ODYS Embedded MPC.
- A black-box nonlinear system identification library to learn neural multivariable autoregressive models from experimental data.
Please contact us at firstname.lastname@example.org for more information about ODYS Deep Learning Toolset.