Periodic Set Invariance as a Tool for Constrained Reference Tracking

Martin Soyer (1) , Sorin Olaru (1) , Konstantinos Ampountolas (2) , Sheila Scialanga (2) , Zhou Fang (3)

1 L2S - Laboratoire des signaux et systèmes
2 University of Glasgow
3 Technocentre Renault [Guyancourt]

This paper explores the concept of periodic invariance and its use for trajectory tracking problems subject to state and input constraints, offering important computational advantages. In principle, traditional techniques based on receding horizon optimization are computationally expensive due to long prediction and optimization horizons, and number of control and state constraints in a constrained control problem. Their complexity is further affected by additional constraints needed to ensure recursive feasibility via a controllable invariant set. Practically, such invariant sets are difficult to obtain off-line and use them on-line. To overcome this problem, this paper suggests to employ periodic invariant sets as a simple set-theoretic tool for constrained reference tracking problems.

Mots clés: Predictive Control; Constrained Control; Periodic Invariance; Reference Tracking

Domaines: Automatique / Robotique; Optimisation et contrôle [math.OC]

Martin Soyer, Sorin Olaru, Konstantinos Ampountolas, Sheila Scialanga, Zhou Fang. Periodic Set Invariance as a Tool for Constrained Reference Tracking. IFAC 2020 - 21st IFAC World Congress, Jul 2020, Berlin (virtual), Germany. ⟨10.1016/j.ifacol.2020.12.384⟩. ⟨hal-02899420⟩