We present a differentiable predictive control (DPC) methodology for learning constrained laws unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit model (MPC). Contrary MPC, does not require supervision by expert controller. Instead, system dynamics is learned the observed system’s dynamics, and neural law optimized offline...
