Deep Learning-based Approximate Nonlinear Model Predictive Control with Offset-free Tracking for Embedded Applications

The implementation of nonlinear model predictive control (NMPC) in applications with fast dynamics remains an open challenge due to the need to solve a potentially non-convex optimization problem in real-time. The offline approximation of NMPC laws using deep learning has emerged as a powerful framework for overcoming these challenges in terms of speed and resource requirements. Deep neural networks (DNNs) are particularly attractive for embedded applications due to their small memory footprint. This work introduces a strategy for achieving offset-free tracking despite the presence of error in DNN-based approximate NMPC. The proposed approach involves a correction factor defined via a small-scale target tracking optimization problem, which is easier to approximate than the tracking NMPC law itself. As such, the overall control strategy is amenable to efficient implementations on low-cost embedded hardware. The effectiveness of the proposed offset-free DNN-based NMPC is demonstrated on a benchmark problem in which the control strategy is deployed onto a field programmable gate array (FPGA) architecture that is verified using hardware-in-the-loop simulations.