Efficient Calibration of Embedded MPC

TL;DR

This paper presents a data-driven optimization method for efficiently tuning embedded Model Predictive Control (MPC) systems, addressing computational constraints and performance trade-offs across different hardware platforms.

Contribution

It introduces a novel global, data-driven approach for tuning embedded MPC controllers considering real-time constraints and hardware limitations.

Findings

Effective tuning of MPC on diverse hardware platforms.

Improved control performance with limited computational resources.

Demonstrated applicability across different embedded systems.

Abstract

Model Predictive Control (MPC) is a powerful and flexible design tool of high-performance controllers for physical systems in the presence of input and output constraints. A challenge for the practitioner applying MPC is the need of tuning a large number of parameters such as prediction and control horizons, weight matrices of the MPC cost function, and observer gains, according to different trade-offs. The MPC design task is even more involved when the control law has to be deployed to an embedded hardware unit endowed with limited computational resources. In this case, real-time system requirements limit the complexity of the applicable MPC configuration, engendering additional design tradeoffs and requiring to tune further parameters, such as the sampling time and the tolerances used in the on-line numerical solver. To take into account closed-loop performance and real-time requirements, in this paper we tackle the embedded MPC design problem using a global, data-driven, optimization approach We showcase the potential of this approach by tuning an MPC controller on two hardware platforms characterized by largely different computational capabilities.