Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems

Understand

We present differentiable predictive control (DPC) as a deep learning-based alternative to the explicit model predictive control (MPC) for unknown nonlinear systems.

• In the DPC framework, a neural state-space model is learned from time-series measurements of the system dynamics.
• The neural control policy is then optimized via stochastic gradient descent approach by differentiating the MPC loss function through the closed-loop system dynamics model.
• The proposed DPC method learns model-based control policies with state and input constraints, while supporting time-varying references and constraints.