{"title":"Robust sampled-time control for a class of constrained nonlinear systems: An interval predictor-based MPC approach","authors":"Ariana Gutiérrez , Héctor Ríos , Manuel Mera , Denis Efimov , Rosane Ushirobira","doi":"10.1016/j.ejcon.2025.101230","DOIUrl":null,"url":null,"abstract":"<div><div>The aim of this study is to design a robust sampled-time controller to stabilize continuous-time nonlinear systems, taking into account state and input constraints, and external disturbances. The proposed strategy is based on two essential components: an interval predictor–based state–feedback controller and a Model Predictive Control (MPC) approach, which deals with the state and input constraints. The interval predictor–based state–feedback controller is designed such that a safe set is provided, where the state constraints are not violated. Such a safe set characterizes the region where the MPC is activated, <em>i.e</em>, out of this set, guaranteeing the fulfillment of the state and input constraints. The proposed switched control strategy ensures the Input–to–State practical Stability of the considered nonlinear systems with respect to external disturbances. To compute the controller gains, a constructive method based on linear matrix inequalities (LMIs) is proposed and the state of the system is not required. The feasibility of the proposed scheme is illustrated by some simulation results.</div></div>","PeriodicalId":50489,"journal":{"name":"European Journal of Control","volume":"84 ","pages":"Article 101230"},"PeriodicalIF":2.5000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Control","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0947358025000585","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The aim of this study is to design a robust sampled-time controller to stabilize continuous-time nonlinear systems, taking into account state and input constraints, and external disturbances. The proposed strategy is based on two essential components: an interval predictor–based state–feedback controller and a Model Predictive Control (MPC) approach, which deals with the state and input constraints. The interval predictor–based state–feedback controller is designed such that a safe set is provided, where the state constraints are not violated. Such a safe set characterizes the region where the MPC is activated, i.e, out of this set, guaranteeing the fulfillment of the state and input constraints. The proposed switched control strategy ensures the Input–to–State practical Stability of the considered nonlinear systems with respect to external disturbances. To compute the controller gains, a constructive method based on linear matrix inequalities (LMIs) is proposed and the state of the system is not required. The feasibility of the proposed scheme is illustrated by some simulation results.
期刊介绍:
The European Control Association (EUCA) has among its objectives to promote the development of the discipline. Apart from the European Control Conferences, the European Journal of Control is the Association''s main channel for the dissemination of important contributions in the field.
The aim of the Journal is to publish high quality papers on the theory and practice of control and systems engineering.
The scope of the Journal will be wide and cover all aspects of the discipline including methodologies, techniques and applications.
Research in control and systems engineering is necessary to develop new concepts and tools which enhance our understanding and improve our ability to design and implement high performance control systems. Submitted papers should stress the practical motivations and relevance of their results.
The design and implementation of a successful control system requires the use of a range of techniques:
Modelling
Robustness Analysis
Identification
Optimization
Control Law Design
Numerical analysis
Fault Detection, and so on.