Real-time measurements of the scheduling parameter of linear parameter-varying (LPV) systems enable the synthesis of robust control invariant (RCI) sets and parameter-dependent controllers inducing invariance. We present a method to synthesize parameter-dependent RCI (PD-RCI) sets for LPV systems with bounded parameter variation, in which invariance is induced using PD-vertex control laws. The PD-RCI sets are parameterized as configuration-constrained polytopes that admit a joint parameterization of their facets and vertices. The proposed sets and associated control laws are computed by solving a single semidefinite programming problem. Through numerical examples, we demonstrate that the proposed method outperforms state-of-the-art methods for synthesizing PD-RCI sets, both with respect to conservativeness and computational load.
Parameter-Dependent Robust Control Invariant Sets for LPV Systems With Bounded Parameter-Variation Rate
Mulagaleti, Sampath Kumar;Mejari, Manas;Bemporad, Alberto
2024-01-01
Abstract
Real-time measurements of the scheduling parameter of linear parameter-varying (LPV) systems enable the synthesis of robust control invariant (RCI) sets and parameter-dependent controllers inducing invariance. We present a method to synthesize parameter-dependent RCI (PD-RCI) sets for LPV systems with bounded parameter variation, in which invariance is induced using PD-vertex control laws. The PD-RCI sets are parameterized as configuration-constrained polytopes that admit a joint parameterization of their facets and vertices. The proposed sets and associated control laws are computed by solving a single semidefinite programming problem. Through numerical examples, we demonstrate that the proposed method outperforms state-of-the-art methods for synthesizing PD-RCI sets, both with respect to conservativeness and computational load.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.