English

Robust linear static anti-windup with probabilistic certificates

Systems and Control 2016-11-18 v3 Optimization and Control

Abstract

In this paper, we address robust static anti-windup compensator design and performance analysis for saturated linear closed loops in the presence of nonlinear probabilistic parameter uncertainties via randomized techniques. The proposed static anti-windup analysis and robust performance synthesis correspond to several optimization goals, ranging from minimization of the nonlinear input/output gain to maximization of the stability region or maximization of the domain of attraction. We also introduce a novel paradigm accounting for uncertainties in the energy of the disturbance inputs. Due to the special structure of linear static anti-windup design, wherein the design variables are decoupled from the Lyapunov certificates, we introduce a significant extension, called scenario with certificates (SwC), of the so-called scenario approach for uncertain optimization problems. This extension is of independent interest for similar robust synthesis problems involving parameter-dependent Lyapunov functions. We demonstrate that the scenario with certificates robust design formulation is appealing because it provides a way to implicitly design the parameter-dependent Lyapunov functions and to remove restrictive assumptions about convexity with respect to the uncertain parameters. Subsequently, to reduce the computational cost, we present a sequential randomized algorithm for iteratively solving this problem. The obtained results are illustrated by numerical examples.

Keywords

Cite

@article{arxiv.1505.06562,
  title  = {Robust linear static anti-windup with probabilistic certificates},
  author = {Simone Formentin and Fabrizio Dabbene and Roberto Tempo and Luca Zaccarian and Sergio M. Savaresi},
  journal= {arXiv preprint arXiv:1505.06562},
  year   = {2016}
}

Comments

13 pages, 7 figures, Accepted for publication as a Regular Paper on the IEEE Transactions on Automatic Control

R2 v1 2026-06-22T09:40:41.630Z