Work

Learning Quasi-LPV Models and Robust Control Invariant Sets With Reduced Conservativeness

Year: 2025

Type: article

Abstract: We present an approach to identify a quasi Linear Parameter Varying (qLPV) model of a plant, with the qLPV model guaranteed to admit a robust control invariant (RCI) set. It builds upon the concurrent synthesis framework presented in Mulagaleti2024qlpv, in which the requirement of existence of an RCI set is modeled as a control-oriented regularization. Here, we reduce the conservativeness of the approach by bounding the qLPV system with an uncertain LTI system, which we derive using bound propagation approaches. The resulting regularization function is the optimal value of a nonlinear robust optimization problem that we solve via a differentiable algorithm. We numerically demonstrate the benefits of the proposed approach over two benchmark approaches.

Source: IEEE Control Systems Letters

DOI: 10.1109/lcsys.2025.3569637

Authors: Sampath Kumar Mulagaleti, Alberto Bemporad

Institution: IMT School for Advanced Studies Lucca

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Research Council, HORIZON EUROPE European Research Council

Awards: G6694132389, Computational model predictive and adaptive control tools