Koopman Model Predictive Control applied in a Damage-tolerant Active strategy

• Grant number: 24/08571-1
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: October 01, 2024
• End date: September 30, 2025
• Field of knowledge: Engineering - Mechanical Engineering - Mechanics of Solids
• Principal Investigator: Samuel da Silva
• Grantee: Fernanda Thaís Colombo
• Supervisor: Nazih Mechbal
• Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil
• Institution abroad: Ecole Nationale Supérieure d'Arts et Métiers, France
• Associated to the scholarship: 22/16271-2 - Damage-tolerant active control in real-time of flexible structures, BP.PD

Abstract

In addition to mitigating unwanted vibration in a structure, damage-tolerant active control (DTAC) seeks to provide appropriate performance even in cases where there is damage. Prior knowledge of the characteristics of the damage that may be imposed on the structure allows the design of controllers that take the effects of the damage into account, but this can be a very limiting assumption. In this project, we propose to apply a strategy based on data from sensors already installed by the control system to reduce vibration and mitigate the impact caused by damage to the structure. First, a data-driven model will be estimated using the Koopman operator that searches for a linear description of the dynamics according to system observations over time. The analysis of the spectral properties of this operator, such as its eigenvalues and eigenvectors, makes it possible to monitor the occurrence of damage to the structure. Second, this linear model of the current dynamics will be combined with a control strategy based on model predictive control, which can incorporate this time-varying model by adjusting the optimization problem at each step. This data-driven strategy will be investigated in a prototype that includes two beams of the same size but with different health conditions. Therefore, this project seeks to bring new results to DTAC by analyzing the system's real-time input and output signals.