Embedded Implementation of a Neural Network emulating Nonlinear MPC in a process control application

We present the design, training, and implementation
of a nonlinear autoregressive neural network for the
control of a multi-input, multi-output hydraulic plant. The
network mimics the optimal control signals of a nonlinear model
predictive controller and is implemented on a low-level microcontroller.
While trained with simulation data only, experiments
on the real plant show that not only the setpoint tracking, but
to some degree also the constraint satisfaction and unmeasured
disturbance rejection are adapted by the neural network. In
contrast to the optimization-based predictive controller, the
neural network easily runs on an ESP32 microcontroller and
Micropython with guaranteed evaluation time and still achieves
similar control performance as the predictive controller.