# Neural network model predictive controller

Nonlinear time-optimal model predictive control (TOMPC) drives constrained systems to a target in minimum time, but online solving is too slow for fast dynamics. This work imitates a long-horizon, nonconvex TOMPC with hard terminal constraints using a neural network, and introduces self-informed sampling: the network also predicts time-to-go, and the expert solver is queried only where a rollout departs from time-optimal behavior. On a Furuta pendulum, this sampling reaches the performance of standard dataset aggregation in roughly one-third of the training time and less than half the data, while the resulting controller keeps the expert's region of attraction and GPU inference raises the control frequency from about 1.4 Hz to 1 kHz.

Nonlinear time-optimal model predictive control (TOMPC) drives constrained systems to a target in minimum time, but online solving is too slow for fast dynamics. This work imitates a long-horizon, nonconvex TOMPC with hard terminal constraints using a neural network, and introduces self-informed sampling: the network also predicts time-to-go, and the expert solver is queried only where a rollout departs from time-optimal behavior. On a Furuta pendulum, this sampling reaches the performance of standard dataset aggregation in roughly one-third of the training time and less than half the data, while the resulting controller keeps the expert's region of attraction and GPU inference raises the control frequency from about 1.4 Hz to 1 kHz.