Embedded Implementation of a Neural Network Controller Emulating Nonlinear MPC in a Process Control Application

On November 14, we organized a scientific seminar on “Embedded Implementation of a Neural Network Controller Emulating Nonlinear MPC in a Process Control Application“. The scientific seminar was led by Sebastian Leonow, a lecturer at Ruhr University Bochum. The scientific seminar was organized in the framework of the FrontSeat project as part of the seminar series on “Research Seminar on Smart Cybernetics”.

Abstract:

We present the design, training, and implementation of a nonlinear autoregressive neural network to control 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 training 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 control performance similar to that of the predictive controller.

This project has received funding from the European Union’s Horizon under grant no. 101079342 (Fostering Opportunities Towards Slovak Excellence in Advanced Control for Smart Industries).