Jonas-Nicodemus/PINNs-based-MPC: First release

We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool for approximating (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not designed to handle variable control actions or variable initial conditions. Thus, we present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs. This enables the controller design based on a PINN as an approximation of the underlying system dynamics. Finally we present our results using our PINN-based MPC to solve a tracking problem for a complex mechanical system, a multi-link manipulator