MPC design for the longitudinal motion of a passenger aircraft based on operator-splitting and fast-gradient methods

This paper describes the design of a model predictive controller (MPC) for the longitudinal motion of a passenger aircraft. The main focus of this work is on the performance of the controller in terms of computation time required for online optimization. In particular, the controller must accomplish the following objectives: (i) run in real-time, i.e., return a command within the sampling time (only a few milliseconds) of the system, and (ii) rely only on simple algebraic operations, in view of future implementations on embedded platforms. We make use of our previously developed parallel dual fast-gradient (PDFG) solver to accomplish the aforementioned objectives. This solver combines operator-splitting and fast-gradient methods and has been successfully tested for regulation problems on academic examples. This work aims to extend the use of the proposed PDFG solver to tracking problems in practical applications, such as the one proposed in this paper. Our results motivate a more extensive investigation of model predictive control techniques in fields with hard real-time constraints, such as aerospace or automotive.