|Personal data||Research themes||Ongoing teaching||Publications|
Person in charge of the Unit : Oui
The research activities of the SAAS group are essentially oriented in two directions: - Supervision and fault diagnosis of technical systems based on mathematical models - Advanced control of nonlinear dynamic system subject to constraints, with emphasis on reference governor methods The developed methods are applied to a wide range of areas including: - Electric energy production, distribution and storage - Mechatronics (UAVs and robotics) - Industrial processes
The aim of this work is designing and constructing a flapping twin-wing robot of the size of hummingbird (Colibri in French) capable of hovering. Our robot currently has a total mass of 22 g, a wing span of 21 cm and a flapping frequency of 22 Hz; it is actively stabilized in pitch, roll and yaw by changing the wing camber with a mechanism known as wing twist modulation. The proposed design of wing twist modulation effectively alters the mean lift vector with respect to the center of gravity by reorganization of the airflow. This mechanism is modulated by an onboard control board which calculates the corrective feedback control signals through a closed-loop controller in order to stabilize the robot. The vertical position is controlled manually by tuning the flapping frequency. The robot has demonstrated successful hovering flights with an on-board battery for the flight autonomy of one minute.
More Grids aims to be a reduction tool for dynamic power system models that enables transmission system operators (TSOs) to carry out stability studies within a time frame compatible with the supervision of an electrical network. The need for simplification is becoming increasingly acute as the size and the complexity of electrical networks continue to grow with the construction of new interconnections. As a result, upholding the same level of accuracy across the whole interconnected network model would not only induce excessively long simulation times, but also go hand in hand with the management of a massive database containing the parameters of all grid components. Given that every TSO must only ensure the stability of its own network, it is realistic to consider a dynamic model in which only the supervised area and its surroundings would maintain the same level of detail, while the remote areas of the interconnected network would be approximated by some simplified models. Developing a tool that designs such models in a systematic way is the main goal of this project.
The main objective of the project is to develop a systematic methodology for the design of robust fault diagnosis systems for large distributed systems. These diagnosis systems must be able to detect and isolate faults that may occur somewhere in a large network. The objectives are the following: – Development of a systematic methodology for the design of robust fault diagnosis systems including systematic structure determination – Development of distributed fault diagnosis systems over networks – Validation of the developed methodology on a test case: battery supervision system for a battery cell first, and for a battery pack next (simulation + experimental setup).
The project aims at developing safer, long lasting and environmentally friendly lithium ion batteries for use in stationary storage applications. The targeted batteries are made of LTO/LFP electrode materials coated onto current collectors via a novel aqueous preparation pathway. These chemistries are indeed known to be more stable and safer than others. However, there is no guarantee that the manufactured battery is the safest or the one with the best performance among different possible designs. Moreover, currently there is no way to track the progression of its internal state as the battery is operated. These two issues can be addressed through the combination of electrochemistry, mathematical modelling and control theory, three domains of expertise covered by the two partners. Three lines of research will be pursued: the optimal design of a battery cell, the study and modelling of aging for such a cell, and the state monitoring of a battery pack. The battery design optimization seeks to improve the battery performance through appropriate sizing. The associated challenges include the choice of relevant design criteria and degrees of freedom to be optimized. The optimized battery design will be experimentally validated by building the cell and verifying the resulting performance index. The aging model will exploit long term cycling experiments to determine aging as a function of the operating conditions. Such information will be used in the battery pack state monitoring system that aims at estimating the state-of charge and state-of-health of the constituent battery cells. These packs arise when series/parallel arrangements of cells are considered in order to meet voltage/power requirements.
The project proposes a concept of piezoelectric adaptive thin shell reflector for future space telescopes; it exhibits excellent areal density and stowability, and thus, paves the way to future large aperture space telescopes. Controlling the surface figure of spherical or parabolic shell with in-plane stresses induced by a piezoelectric layer raises two problems: (1) Doubly curved shells are significantly stiffer than flat plates and (2) When using segmented electrodes with different voltages, the surface figure is subject to edge fluctuations. This results in a very large number of electrodes, leading to ill-conditioning in the Jacobian matrix of the system; to solve this, a hierarchical approach is proposed to inverse the Jacobian, based on Saint-Venant’s principle. This research also provides a petal configuration design which aims at reducing the hoop stiffness and improving the foldability of the reflector.
The use of an electrochemical model (EChM) to represent a battery cell allows obtaining accurate estimation of its state of charge (SOC) and state of heath (SOH). In this way it is possible to extend the limits of usage of the battery while ensuring safe conditions of work. This requires formulating and imposing specific constraints on the electrochemical states. To this end a constrained controller is needed. The challenges from the control point of view stems from the nature of the model and the available measurements. Indeed, the EChM is made of a set of partial differential equations coupled by a highly nonlinear output function, the electrochemical parameters are estimated and only the current in input (I) and the voltage in output (V) of the battery are known. The problem is to find a suitable solution that can use the advantages of an EChM within a constrained control design framework. The next step is to extend the results to manage a battery pack.
This research project is part of the Belgian PhairywinD project, which aims to develop the current and the future offshore wind farms in a multi-disciplinary framework. The ability to participate in the frequency regulation and provide ancillary services to the TSO (Transmission System Operator) is one of the present offshore wind farm challenges. The goal of this research is to further investigate how wind farms can achieve this goal at best by taking into account wake effects, load mitigation and active power reserve in the dispatching of the active power set points to the turbines. The control strategy will be based on the following modules : a module estimating the total power available in the immediate future, a module deducing the plant wide power reserve needed to ensure proper frequency regulation, and a module performing the optimal power dispatching. The developed control strategy will be implemented and validated in simulation using software like FAST.farm and SOWFA.
The trend is to replace the hydraulic actuators by electromechanical actuators (EMAs) in aviation. This structural change would allow cancelling the overall hydraulic circuit on board and thus decrease the maintenance needs, the energy consumption and the aircraft weight. MONISA aims at designing, implementing and validating a monitoring system for EMAs used in aircraft flight surface control. Such a system will allow keeping an identical level of safety and availability for the EMAs as for hydraulic actuators by following their health status. It should be able to detect in a premature way any fault occurrence on the EMAs and follow its temporal evolution. In that way maintenance operations can be performed in due time to avoid that the degradations lead to a functional failure of the actuator. Furthermore, the monitoring system must have a very low false alarm probability in order not affect aircraft availability.
This project introduces a fault detection and isolation (FDI) system for wind farms based on SCADA (Supervisory Control and Data Acquisition) data. Instead of the traditional approach of monitoring each turbine individually, this project proposes a model-based FDI system that exploits correlations between measurements associated to neighboring turbines in order to detect and localize possible faults. This feature could enhance the performance of the FDI system, in terms of its capability for early detection of faults and the reduction of false detection and missed detection occurrences. The main challenge is to account for the difference in wind conditions that each turbine is subject to, which can significantly modify the relative turbine behaviors. To this end, linear parameter-varying 2(LPV) models will be used to represent the relative dynamics between the turbines as a function of wind speed and wind direction, notably. These LPV models will be identified in the frequency domain since the modeling can be done in a user defined frequency band. Also, the continuous-time framework will be chosen because models related with the physics of the system can facilitate the design of the FDI system. The project will be carried out as follows. First, a LPV model for monitoring a single wind turbine will be developed with focus on the pitch system. Second, the results obtained are extended for the LPV modelling of wind farms with focus on the data associated to turbine efficiency and overheating. Third, the obtained models are analyzed and a FDI system for wind farms is designed. This brings additional challenges to be addressed: how to best construct the LPV models for enhancing fault detection while managing complexity? How to automatically extract relevant data for the modeling? How to ensure a systematic design and tuning of the FDI system? Finally, the validation on a wind farm simulator and with SCADA data from an on-shore wind farm made of ten 2.5 MW turbines will be performed.
Project PANTHEON is a research project funded by the European Commission within the Horizon 2020 framework programme. Inspired by the real needs of the partner Ferrero, the project aims at defining a new paradigm for the precision farming of hazelnut orchards. The core idea is to build a system able to monitor the phytosanitary status of each single plant of the orchard. This will result in focused interventions, yielding an increase in the overall orchard production while being more cost-effective and environmentally friendly. The PANTHEON consortium is coordinated by Prof. A. Gasparri and consists of four universities (Roma Tre, ULB, Trier, and Tuscia) and two industrial partners (Sigma Consulting, and Ferrero), which provide the complementary expertise and technologies (ranging from robotics and control theory, to agronomy, remote sensing, and big data) needed for the success of the project. For more information : https://www.facebook.com/ProjectPantheon/ and https://twitter.com/ProjectPantheon