Computation of Parameter Dependent Robust Invariant Sets for LPV Models with Guaranteed Performance
Preprint, 2020

This paper presents an iterative algorithm to compute a Robust Control Invariant (RCI) set, along with an invariance-inducing control law, for Linear Parameter-Varying (LPV) systems. As the real-time measurements of the scheduling parameters are typically available, in the presented formulation, we allow the RCI set description along with the invariance-inducing controller to be scheduling parameter dependent. The considered formulation thus leads to parameter-dependent conditions for the set invariance, which are replaced by sufficient Linear Matrix Inequality (LMI) conditions via Polya's relaxation. These LMI conditions are then combined with a novel volume maximization approach in a Semidefinite Programming (SDP) problem, which aims at computing the desirably large RCI set. In addition to ensuring invariance, it is also possible to guarantee performance within the RCI set by imposing a chosen quadratic performance level as an additional constraint in the SDP problem. The reported numerical example shows that the presented iterative algorithm can generate invariant sets which are larger than the maximal RCI sets computed without exploiting scheduling parameter information.

Semi-definite programming

Invariant set

Linear parameter-varying systems.

Linear matrix inequalities

Author

Ankit Gupta

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Manas Mejari

IDSIA (Dalle Molle Institute for Artificial Intelligence)

Paolo Falcone

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Dario Piga

IDSIA (Dalle Molle Institute for Artificial Intelligence)

Vehicle motion control with performance and safety guarantees

VINNOVA, 2015-09-01 -- 2019-08-31.

Driving Forces

Sustainable development

Subject Categories

Computational Mathematics

Control Engineering

Signal Processing

Roots

Basic sciences

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Latest update

9/22/2020