Work

Model predictive control for tracking with implicit invariant sets

Year: 2025

Type: article

Abstract: This paper presents a model predictive control (MPC) technique for tracking with implicit terminal components. The controller formulation includes an artificial setpoint as decision variable, and the terminal constraint is defined implicitly for an augmented system that depends on the latter. In this respect, instead of computing an invariant terminal set, we consider an extended prediction horizon whose length can be bounded simply by solving LPs. This approach overcomes size-related limitations associated with the operations needed for computing invariant sets, also simplifying the offline MPC design. The proposed controller is able to drive large systems to admissible setpoints while guaranteeing recursive feasibility and convergence. Finally, the method is illustrated by an academic example, a mass–spring–damper system of variable-size and a more realistic case study of a drone.

Source: Automatica

DOI: 10.1016/j.automatica.2025.112436

Authors: Irene Luque, Paula Chanfreut, Daniel Limón, José M. Maestre

Institutions: Universidad de Sevilla, Eindhoven University of Technology

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,760

Funders: Ministerio de Ciencia, Innovación y Universidades, Centers for Disease Control and Prevention, European Regional Development Fund, Agencia Estatal de Investigación