Riccardo M.G. Ferrari | publications

Jump to journals, books, conferences and other publications. You can also see my full publications list on my Orcid, Google Scholar or Scopus profile:

journals

2024

2023

CONENGPRAC

A Sliding Mode Observer Approach to Oscillatory Fault Detection in Commercial Aircraft

Keijzer, Twan, Engelbrecht, Japie A. A., Goupil, Philippe, and Ferrari, Riccardo M.G.

Control Engineering Practice 2023

DOI
IEEE_LCSS

Secure State Estimation under Actuator and Sensor Attacks using Sliding Mode Observers

Keijzer, Twan, Ferrari, Riccardo M.G., and Sandberg, Henrik

IEEE Control Systems Letters 2023
WE

Convex Economic Model Predictive Control for Blade Loads Mitigation on Wind Turbines

Pamososuryo, Atindriyo, Liu, Yichao, Hovgaard, Tobias, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

Wind Energy 2023

DOI
RENENE

Wind farm control for wake-loss compensation, thrust balancing and load-limiting of turbines

Silva, Jean Gonzalez, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

Renewable Energy 2023

Abs DOI

As renewable energy sources such as wind farms become dominant, new challenges emerge for operating and controlling them. Traditionally, wind farm control aims to dispatch power set-points to individual turbines to maximize energy extraction and, thus, their usage as assets. Yet, grid balance and frequency support are fundamental in presence of high renewable penetration and volatility of energy prices and demand. This requires a paradigm change, moving from power maximization to revenue maximization. In this paper, three active power control strategies pushing this shift of paradigm are investigated, namely: wake-loss compensation, thrust balancing, and load-limiting control. The findings of large eddy simulations of a reference wind farm show that wake-loss compensation indeed improves the power generation on waked wind farms, but at the price of increased structural loads on certain turbines. The addition of a thrust balancing can equalize the stresses of individual turbines and their wear in the long term, while still attaining the required power output at the farm level. Furthermore, load-limiting controllers could potentially aid by allowing maintenance to be scheduled in a single time window, thus reducing operation and maintenance costs.

2022

AUTO

Threshold Design for Fault Detection with First Order Sliding Mode Observers

Keijzer, Twan, and Ferrari, Riccardo M.G.

Automatica 2022

Abs DOI

Sliding Mode Observer (SMO) based methods have been extensively used for Fault Estimation (FE). However, the fault detection (FD) problem for these SMO based FE methods has not been completely solved. In this paper a robust threshold on the so-called Equivalent Output Injection (EOI) is presented which enables FD for systems with measurement noise and unmatched uncertainties. This threshold is applicable to a large class of existing SMO based FE methods, and its applicability can easily be verified. Theoretical guarantees on the detection performance of this threshold are provided, and further demonstrated via a simulation study.
ENRG

Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study

Barber, Sarah, Lima, Luiz Andre Moyses, Sakagami, Yoshiaki, Quick, Julian, Latiffianti, Effi, Liu, Yichao, Ferrari, Riccardo M.G., Letzgus, Simon, Zhang, Xujie, and Hammer, Florian

Energies 2022

Abs DOI

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers.
IEEE_LCSS

The Proportional Integral Notch and Coleman Blade Effective Wind Speed Estimators and Their Similarities

Liu, Yichao, Pamososuryo, Atindriyo Kusumo, Mulders, Sebastiaan P., Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

IEEE Control Systems Letters 2022

Abs arXiv DOI

The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the Blade Effective Wind Speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: A Proportional Integral Notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the non-rotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well behaved transient response than the PIN estimator.
IEEE_LCSS

The Immersion and Invariance Wind Speed Estimator Revisited and New Results

Liu, Yichao, Pamososuryo, Atindriyo Kusumo, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

IEEE Control Systems Letters 2022

arXiv DOI
WES

Load Reduction for Wind Turbines: an Output Constrained, Subspace Predictive Repetitive Control Approach

Liu, Yichao, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

Wind Energy Science Discussions 2022

DOI

2021

IET_RPG

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and Fisher discriminant analysis

Wu, Ping, Liu, Yichao, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

IET Renewable Power Generation 2021

Abs DOI

Abstract Over the past decades, Floating Offshore Wind Turbine (FOWT) has gained increasing attention in wind engineering due to the rapidly growing energy demands. However, difficulties in turbine maintenance will increase due to the harsh operational conditions. Fault diagnosis techniques play a crucial role to enhance the reliability of FOWTs and reduce the cost of offshore wind energy. In this paper, a novel data-driven fault diagnosis method using regularized dynamic canonical correlation analysis (RDCCA) and Fisher discriminant analysis (FDA) is proposed for FOWTs. Specifically, to overcome the collinearity problem that exists in measured process data, dynamic canonical correlation analysis with a regularization scheme, is developed to exploit the relationship between input and output signals. Then, the residual signals are generated from the established RDCCA model for fault detection. To further classify the fault type, an FDA model is trained from the residual signals of different training faulty data sets. Simulations on a FOWT baseline model based on the widely used National Renewable Energy Laboratory FAST simulator are carried out to demonstrate the feasibility and efficacy of the proposed fault detection and classification method. Results have shown many salient features of the proposed method with potential applications in FOWTs.
J_AST

System analysis and controller design for the electric pump of a deep-throttling rocket engine

Hu, Runsheng, Ferrari, Riccardo M.G., Chen, Zhenyu, Cheng, Yuqiang, Zhu, Xiaobin, Cui, Xing, and Wu, Jianjun

Aerospace Science and Technology 2021

Abs DOI

This paper proposes a controller design for the electric pump of a deep-throttling rocket engine. The nonlinearity of the system is taken into consideration by analyzing the gap metric. Then, proportional-integral-derivative controller and gain-scheduling linear quadratic regulator are designed. Analyzing the amplitude- and phase-frequency characteristics as well as the pole-zero distribution of the system, the results show that the designed controllers can stabilize the linearized equations in incremental form at different operating points. This indicates that these two controllers are available for the original system in the whole range of working conditions and this is verified in the simulation. Meanwhile, the comparison between proportional-integral-derivative controller and gain-scheduling linear quadratic regulator is presented. It demonstrates that the proportional-integral-derivative controller is better at tracking both step and ramp signals but with worse control signals. It means that the proportional-integral-derivative controller seems less suitable for real use due to severe oscillations. Meanwhile, the parameter tuning of a proportional-integral-derivative controller depends on more extensive manual tuning. Therefore, the gain-scheduling linear quadratic regulator is preferred.
WE

Fault-Tolerant Individual Pitch Control of Floating Offshore Wind Turbines via Subspace Predictive Repetitive Control

Liu, Yichao, Frederik, Joeri, Ferrari, Riccardo M.G., Wu, Ping, Li, Sunwei, and Wingerden, Jan-Willem

Wind Energy 2021

DOI
J_RENENE

Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach

Liu, Yichao, Ferrari, Riccardo M.G., Wu, Ping, Jiang, Xiaoli, Li, Sunwei, and Wingerden, Jan-Willem

Renewable Energy 2021

DOI
IEEE_TII

Data Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis

Wu, Ping, Liu, Yichao, and Ferrari, Riccardo M.G.

IEEE Transactions on Industrial Informatics 2021

DOI
IEEE_TAC

A Switching Multiplicative Watermarking Scheme for Detection of Stealthy Cyber-Attacks

Ferrari, Riccardo M.G., and Teixeira, André M.H.

IEEE Transactions on Automatic Control 2021

DOI

2020

IEEE_TAC

Privatized distributed anomaly detection for large-scale nonlinear uncertain systems

Rostampour, Vahab, Ferrari, Riccardo M.G., Teixeira, André M.H., and Keviczky, Tamas

IEEE Transactions on Automatic Control 2020

DOI
IEEE_RAL

A Novel Adaptive Controller for Robot Manipulators based on Active Inference

Pezzato, Corrado, Ferrari, Riccardo M.G., and Hernández Corbato, Carlos

IEEE Robotics and Automation Letters 2020

DOI

2017

IEEE_TAC

A Distributed Networked Approach for Fault Detection of Large-scale Systems

Boem, Francesca, Ferrari, Riccardo M.G., Keliris, C., Parisini, Thomas, and Polycarpou, Marios M.

IEEE Transactions on Automatic Control 2017

Abs DOI

Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique.

2013

ARC

Distributed fault diagnosis for continuous-time nonlinear systems: The input–output case

Boem, Francesca, Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

Annual Reviews in Control 2013

Abs DOI

Abstract In this paper, some new results on distributed fault diagnosis of continuous-time nonlinear systems with partial state measurements are proposed. By exploiting an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical system is described as the interconnections of several subsystems. Each subsystem is monitored by a Local Fault Diagnoser: a set of local estimators, based on the nominal local dynamic model and on an adaptive approximation of the interconnection and of the fault function, allows to derive a local fault decision. A consensus-based protocol is used in order to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition ensuring the convergence of the estimation errors is derived. Finally, possibly non-conservative time-varying threshold functions guaranteeing no false-positive alarms and theoretical results dealing with detectability and isolability sufficient conditions are presented.

2012

IEEE_TAC

Distributed Fault Detection and Isolation of Large-Scale Discrete-Time Nonlinear Systems: An Adaptive Approximation Approach

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

IEEE Transactions on Automatic Control 2012

Abs DOI

This paper deals with the problem of designing a distributed fault detection and isolation methodology for nonlinear uncertain large-scale discrete-time dynamical systems. As a divide et impera approach is used to overcome the scalability issues of a centralized implementation, the large scale system being monitored is modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a Local Fault Diagnoser is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability and isolability of faults affecting variables shared among overlapping subsystems. Theoretical results are provided to characterize the detection and isolation capabilities of the proposed distributed scheme. Finally, simulation results are reported showing the effectiveness of the proposed methodology.

2011

EJC

Distributed Fault Detection and Isolation of Continuous-Time Non-Linear Systems

Boem, Francesca, Ferrari, Riccardo M.G., and Parisini, Thomas

European Journal of Control 2011

Abs DOI

This paper presents a continuous-time distributed fault detection and isolation methodology for nonlinear uncertain possibly large-scale dynamical systems. The monitored system is modeled as the interconnection of several subsystems and a divide et impera approach using an overlapping decomposition is adopted. Each subsystem is monitored by a Local Fault Diagnoser using the information based on the measured local state of the subsystem as well as the measurements about neighboring states thanks to the subsystem interconnections. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. In order to improve the detectability and isolability of faults affecting variables shared among different subsystems, a consensus-based estimator is designed. Theoretical results are provided to characterize the detection and isolation capabilities of the proposed distributed scheme.

2009

IEEE_TAC

Distributed Fault Diagnosis With Overlapping Decompositions: An Adaptive Approximation Approach

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

IEEE Transactions on Automatic Control 2009

Abs DOI

This technical note deals with the problem of designing a distributed fault detection methodology for distributed (and possibly large-scale) nonlinear dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a local fault detector is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability of faults affecting variables shared among different subsystems. Simulation results provide an evidence of the effectiveness of the proposed distributed fault detection scheme.

book and book chapters

2021

SPRINGER

Safety, security, and privacy for cyber-physical systems

Springer 2021

Website
SPRINGER

Introduction

Ferrari, Riccardo M.G., and Teixeira, André M.H.

In Safety, security, and privacy for cyber-physical systems, Springer 2021

Website
SPRINGER

Detection of Cyber-Attacks: a Multiplicative Watermarking Scheme

Ferrari, Riccardo M.G., and Teixeira, André M.H.

In Safety, security, and privacy for cyber-physical systems, Springer 2021

Website
SPRINGER

Differentially Private Anomaly Detection for Interconnected Systems

Ferrari, Riccardo M.G., Degue, Kwassi H, and Le Ny, Jerome

In Safety, security, and privacy for cyber-physical systems, Springer 2021

Website
SPRINGER

Conclusions

Ferrari, Riccardo M.G.

In Safety, security, and privacy for cyber-physical systems, Springer 2021

Website

conferences

2024

ECC24

Transient Solutions of the Fokker-Planck Equation using a Galerkin-Method with Weighted Test Functions

Martens, Walter, and Ferrari, Riccardo M.G.

In European Control Conference 2024
ECC24

Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares

Wang, Yang, Ferrari, Riccardo M.G., and Verhaegen, Michel

In European Control Conference 2024

2023

JPCS

A FAST. Farm and MATLAB/Simulink interface for wind farm control design

Smits, Coen-Jan, Gonzales-Silva, Jean, Chabaud, Valentin, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Journal of Physics: Conference Series 2023

DOI
VPPC23

Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach

Desai, Tushar, and Ferrari, Riccardo M.G.

In IEEE Vehicle Power and Propulsion Conference (VPPC) 2023

DOI
CDC23

Secure State Estimation under Actuator and Sensor Attacks using Sliding Mode Observers

Keijzer, Twan, Ferrari, Riccardo M.G., and Sandberg, Henrik

In Conference on Decision and Control 2023
CDC23

Hybrid Design of Multiplicative Watermarking for Defense Against Malicious Parameter Identification

Zhang, Jiaxuan, Gallo, Alexander J., and Ferrari, Riccardo M.G.

In Conference on Decision and Control 2023

arXiv
IFAC23

UKF-based Wind Estimation and Sub-optimal Turbine Control under Waked Conditions

Gonzales-Silva, Jean, Liu, Yichao, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In IFAC World Congress 2023
IFAC23

Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

Desai, Tushar, Oliva, Federico, Ferrari, Riccardo M.G., and Carnevale, Daniele

In IFAC World Congress 2023
IFAC23

A Coalitional MPC Approach to Control of Collaborative Vehicle Platoons

Chanfreut, Paula, Keijzer, Twan, Maestre, José Maria, and Ferrari, Riccardo M.G.

In IFAC World Congress 2023

2022

AWEC

Comparison of Two Data-driven Airborne Wind Energy Oriented Long-term Weather Forecast Methods

Feng, Zhixin, Wan, Jia, Ompusunggu, Agusmian Partogi, and Ferrari, Riccardo M.G.

2022
CDC22

An Economic Model Predictive Control Approach for Load Mitigation on Multiple Tower Locations of Wind Turbines

Feng, Zhixin, Gallo, Alexander J., Liu, Yichao, Pamososuryo, Atindriyo Kusumo, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Conference on Decision and Control 2022

Abs arXiv

The current trend in the evolution of wind tur- bines is to increase their rotor size in order to capture more power. This leads to taller, slender and more flexible towers, which thus experience higher dynamical loads due to the tur- bine rotation and environmental factors. It is hence compelling to deploy advanced control methods that can dynamically counteract such loads, especially at tower positions that are more prone to develop cracks or corrosion damages. Still, to the best of the authors’ knowledge, little to no attention has been paid in the literature to load mitigation at multiple tower locations. Furthermore, there is a need for control schemes that can balance load reduction with optimization of power production. In this paper, we develop an Economic Model Predictive Control (eMPC) framework to address such needs. First, we develop a linear modal model to account for the tower flexural dynamics. Then we incorporate it into an eMPC framework, where the dynamics of the turbine rotation are expressed in energy terms. This allows us to obtain a convex formulation, that is computationally attractive. Our control law is designed to avoid the “turn-pike” behavior and guarantee recursive feasibility. We demonstrate the performance of the proposed controller on a 5MW reference WT model: the results illustrate that the proposed controller is able to reduce the tower loads at multiple locations, without significant effects to the generated power.
CDC22

A Fully Homomorphic Encryption Scheme for Real-Time Safe Control

Stobbe, Pieter, Keijzer, Twan, and Ferrari, Riccardo M.G.

In Conference on Decision and Control 2022

Abs arXiv

Fully Homomorphic Encryption (FHE) has made it possible to perform addition and multiplication operations on encrypted data. Using FHE in control thus has the advantage that control effort for a plant can be calculated remotely without ever decrypting the exchanged information. FHE in its current form is however not practically applicable for real-time control as its computational load is very high compared to traditional encryption methods. In this paper a reformulation of the Gentry FHE scheme is proposed and applied on an FPGA to solve this problem. It is shown that the resulting FHE scheme can be implemented for real-time stabilization of an inverted double pendulum using discrete time control.
CDC22

Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines

Gonzales-Silva, Jean, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Conference on Decision and Control 2022

Abs arXiv

Wind turbine (WT) controllers are often geared towards maximum power extraction, while suitable operating constraints should be guaranteed such that WT components are protected from failures. Control strategies can be also devised to reduce the generated power, for instance to track a power reference provided by the grid operator. They are called down- regulation strategies and allow to balance power generation and grid loads, as well as to provide ancillary grid services, such as frequency regulation. Although this balance is limited by the wind availability and grid demand, the quality of wind energy can be improved by introducing down-regulation strategies that make use of the kinetic energy of the turbine dynamics. This paper shows how the kinetic energy in the rotating components of turbines can be used as an additional degree-of-freedom by different down-regulation strategies. In particular we explore the power tracking problem based on convex model predictive control (MPC) at a single wind turbine. The use of MPC allows us to introduce a further constraint that guarantees flow stability and avoids stall conditions. Simulation results are used to illustrate the performance of the developed down- regulation strategies. Notably, by maximizing rotor speeds, and thus kinetic energy, the turbine can still temporarily guarantee tracking of a given power reference even when occasional saturation of the available wind power occurs. In the study case we proved that our approach can guarantee power tracking in saturated conditions for 10 times longer than with traditional down-regulation strategies.
CDC22

Hierarchical Cyber-Attack Detection in Large-Scale Interconnected Systems

Keijzer, Twan, Gallo, Alexander J., and Ferrari, Riccardo M.G.

In Conference on Decision and Control 2022

Abs arXiv

In this paper we present a hierarchical scheme to detect cyber-attacks in a hierarchical control architecture for large-scale interconnected systems (LSS). We consider the LSS as a network of physically coupled subsystems, equipped with a two-layer controller: on the local level, decentralized controllers guarantee overall stability and reference tracking; on the supervisory level, a centralized coordinator sets references for the local regulators. We present a scheme to detect attacks that occur at the local level, with malicious agents capable of affecting the local control. The detection scheme is computed at the supervisory level, requiring only limited exchange of data and model knowledge. We offer detailed theoretical analysis of the proposed scheme, highlighting its detection properties in terms of robustness, detectability and stealthiness conditions.
RENEW22

A control-oriented wind turbine dynamic simulation framework which resolves local atmospheric conditions

Feng, Zhixin, Ferrari, Riccardo M.G., Wingerden, Jan-Willem, and Liu, Yichao

In Trends in Renewable Energies Offshore: Proceedings of the 5th International Conference on Renewable Energies Offshore (RENEW 2022, Lisbon, Portugal, 8–10 November 2022) 2022

Abs arXiv DOI

In this paper we present a hierarchical scheme to detect cyber-attacks in a hierarchical control architecture for large-scale interconnected systems (LSS). We consider the LSS as a network of physically coupled subsystems, equipped with a two-layer controller: on the local level, decentralized controllers guarantee overall stability and reference tracking; on the supervisory level, a centralized coordinator sets references for the local regulators. We present a scheme to detect attacks that occur at the local level, with malicious agents capable of affecting the local control. The detection scheme is computed at the supervisory level, requiring only limited exchange of data and model knowledge. We offer detailed theoretical analysis of the proposed scheme, highlighting its detection properties in terms of robustness, detectability and stealthiness conditions.
IROS22

Unbiased Active Inference for Classical Control

Baioumy, Mohamed, Pezzato, Corrado, Ferrari, Riccardo M.G., and Hawes, Nick

In International Conference on Intelligent Robots and Systems 2022

Abs arXiv

Active inference is a mathematical framework that originated in computational neuroscience. Recently, it has been demonstrated as a promising approach for constructing goal-driven behavior in robotics. Specifically, the active inference controller (AIC) has been successful on several continuous control and state-estimation tasks. Despite its relative success, some established design choices lead to a number of practical limitations for robot control. These include having a biased estimate of the state, and only an implicit model of control actions. In this paper, we highlight these limitations and propose an extended version of the unbiased active inference controller (u-AIC). The u-AIC maintains all the compelling benefits of the AIC and removes its limitations. Simulation results on a 2-DOF arm and experiments on a real 7-DOF manipulator show the improved performance of the u-AIC with respect to the standard AIC. The code can be found at https://github.com/cpezzato/unbiased_aic.
CCTA22

Autonomous RC Cars for Control Research and Education: Implementation of Virtual Potential Based Navigation and Platooning

De Jager, TR, Meinders, NK, Van Vugt, TA, and Zomerdijk, W

In 2022 IEEE Conference on Control Technology and Applications (CCTA) 2022
ACC22

The Proportional Integral Notch and Coleman Blade Effective Wind Speed Estimators and Their Similarities

Liu, Yichao, Pamososuryo, Atindriyo Kusumo, Mulders, Sebastiaan P., Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In American Control Conference 2022

Abs arXiv

The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore limits the attainable levels of load mitigation, and the estimation of the blade effective wind speed (BEWS) might present opportunities for improved load control. This letter introduces two novel BEWS estimator approaches: a proportional-integral-notch (PIN) estimator based on individual blade load measurements, and a Coleman estimator targeting the estimation in the nonrotating frame. Given the seeming disparities between these two estimators, the objective of this letter is to analyze the similarities between the approaches. It is shown that the PIN estimator, which is equivalent to the diagonal form of the Coleman estimator, is a simple but effective method to estimate the BEWS. The Coleman estimator, which takes the coupling effects between individual blades into account, shows a more well-behaved transient response than the PIN estimator.
ACC22

A Switching Thrust Tracking Controller for Load Constrained Wind Turbines

Gonzales-Silva, Jean, Hoek, Daan, Mulders, Sebastiaan P., Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In American Control Conference 2022

Abs

Wind turbines are prone to structural degradation, particularly in offshore locations. Based on the structural health condition of the tower, power de-rating strategies can be used to reduce structural loads at the cost of power losses. This paper introduces a novel closed-loop switching control architecture to constrain the thrust in individual turbines. By taking inspiration from developments in the field of reference governors, an existing demanded power tracking controller is extended by a thrust tracking controller. The latter is activated only when a user-defined constraint on fore-aft thrust force is exceeded, which can be set based on the actual damage status of the turbine. Having a down-regulation with monotonic aerodynamic load response, a simple linear thrust tracking controller is proposed. Such a scheme can reduce aerodynamic loads while incurring acceptable losses on power production which, in a wind farm setting, can be compensated for by other turbines. Large eddy simulations demonstrate the performance of the proposed scheme on satisfying thrust constraints.
ACC22

Periodic Load Estimation of a Wind Turbine Tower using a Model Demodulation Transformation

Pamososuryo, Atindriyo Kusumo, Mulders, Sebastiaan P., Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In American Control Conference 2022

Abs

Individual Pitch Control (IPC) is an effective control strategy to mitigate the blade loads on large-scale wind turbines. Since IPC usually requires high pitch actuation, the safety constraints of the pitch actuator should be taken into account when designing the controller. This paper introduces a constrained Subspace Predictive Repetitive Control (SPRC) approach, which considers the limitation of blade pitch angle and pitch rate. To fulfill this goal, a model predictive control scheme is implemented in the fully data-driven SPRC approach to incorporate the physical limitations of the pitch actuator in the control problem formulation. An optimal control law subjected to constraints is then formulated so that future constraint violations are anticipated and prevented. Case studies show that the developed constrained SPRC reduces the pitch activities necessary to mitigate the blade loads when experiencing wind turbulence and abrupt wind gusts. More importantly, the approach allows the wind farm operator to design conservative bounds for the pitch actuator constraints that satisfies safety limitations, design specifications and physical restrictions. This will help to alleviate the cyclic fatigue loads on the actuators, increase the structural reliability and extend the lifespan of the pitch control system.
SAFEPROCESS22

Cryptographic switching functions for multiplicative watermarking in cyber-physical systems

Gallo, Alexander J., and Ferrari, Riccardo M.G.

In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2022

Abs arXiv

In this paper we present a novel switching function for multiplicative watermarking systems. The switching function is based on the algebraic structure of elliptic curves over finite fields. The resulting function allows for both watermarking generator and remover to define appropriate system parameters, sharing only limited information, namely a private key. We prove that the resulting watermarking parameters lead to a stable watermarking scheme.
SAFEPROCESS22

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Baioumy, Mohamed, Hartemink, William, Ferrari, Riccardo M.G., and Hawes, Nick

In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2022

Abs arXiv video

Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault- tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.
TORQUE22

Active power control of wind farms: an instantaneous approach on waked conditions

Gonzales-Silva, Jean, Doekemeijer, Bart Matthijs, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Science of Making Torque from Wind Conference 2022

Abs DOI

This paper presents a closed-loop controller for wind farms to provide active power control services using a high-fidelity computational fluid dynamics based wind plant simulator. The proposed design enhances power tracking stability and allows for simple understanding, where each turbine is considered as a pure time-delay system. The paper investigates the control performance with different nominal power distributions in a fully waked condition and limited power availability. Results demonstrate the improvement in power production obtained by closing the control loop, compared to greedy operation. Additionally, power tracking capabilities are enhanced with a nominal power distribution favored by axial-induction, as well as the occurrence of turbine saturation and the distribution of loads.
TORQUE22

Individual pitch control by convex economic model predictive control for wind turbine side-side tower load alleviation

Pamososuryo, Atindriyo Kusumo, Liu, Yichao, Hovgaard, Tobias Gybel, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Science of Making Torque from Wind Conference 2022

Abs DOI

The wind turbine side-side tower motion is known to be lightly damped. One viable active damping solution is realized by deploying individual pitch control (IPC) such that counteracting blade forces are created to alleviate the tower fatigue loading caused by this motion. Existing IPC methods for side-side tower damping in the literature, such as linear quadratic regulator and lead-lag controller, cannot accommodate direct optimization and trade- off tunings of the wind turbine economic performance. In this work, a novel side-side tower damping IPC strategy under a convex economic model predictive control (CEMPC) framework is therefore developed to address these challenges. The main idea of the framework lies in the variable transformation in power and energy terms to obtain linear dynamics and convex constraints, over which the economic performance of the wind turbine is maximized with a globally optimal solution in a receding horizon manner. The effectiveness of the proposed method is showcased in a high-fidelity simulation environment under both steady and turbulent wind cases. Lower fatigue damage on the side-side tower bending moment is attained with an acceptable level of pitch activities, negligible impact on the blade loads, and minor improvement on the power production.
ICASSP22

Wave-Domain Approach For Cancelling Noise Entering Open Windows

Ratering, Daan, Kleijn, Bastiaan W., Gonzales-Silva, Jean, and Ferrari, Riccardo M.G.

In 2022 IEEE International Conference on Acoustics, Speech and Signal Processing 2022

DOI

2021

CDC21

The Immersion and Invariance Wind Speed Estimator Revisited and New Results

Liu, Yichao, Pamososuryo, Atindriyo Kusumo, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Conference on Decision and Control 2021

arXiv DOI
CDC21

Design of multiplicative watermarking against covert attacks

Gallo, Alexander J., Anand, Sribalaji C., Teixeira, André M.H., and Ferrari, Riccardo M.G.

In Conference on Decision and Control 2021

arXiv DOI
IWAI21

Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference

Baioumy, Mohamed, Pezzato, Corrado, Corbato, Carlos Hernández, Hawes, Nick, and Ferrari, Riccardo M.G.

In International Workshop on Active Inference 2021

arXiv DOI
WESC21

Wind turbine control using a WRF-based meteorological model: a case study on a 5MW benchmark

Feng, Zhixin, Liu, Yichao, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Wind Energy Science Conference 2021
ECC21

Blade Effective Wind Speed Estimation: A Subspace Predictive Repetitive Estimator Approach

Liu, Yichao, Pamososuryo, Atindriyo Kusumo, Ferrari, Riccardo M.G., Hovgaard, Tobias Gybel, and Wingerden, Jan-Willem

In European Control Conference 2021

arXiv DOI video
ECC21

Fault-tolerant Control of Robotic Systems with Sensory Faults using Unbiased Active Inference

Baioumy, Mohamed, Pezzato, Corrado, Ferrari, Riccardo M.G., Corbato, Carlos Hernández, and Hawes, Nick

In European Control Conference 2021

arXiv DOI video
ECC21

Active Power Control of Waked Wind Farms: Compensation of Turbine Saturation and Thrust Force Balance

Gonzales-Silva, Jean, Doekemeijer, Bart Matthijs, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In European Control Conference 2021

arXiv DOI video
ECC21

Detection of Cyber-Attacks in a Collaborative Intersection Control Scenario

Keijzer, Twan, Jarmolowitz, Fabian, and Ferrari, Riccardo M.G.

In European Control Conference 2021

arXiv DOI video
ECC21

Detection of Network and Sensor Cyber-Attacks in Platoons of Cooperative Autonomous Vehicles: a Sliding-Mode Observer Approach

Keijzer, Twan, and Ferrari, Riccardo M.G.

In European Control Conference 2021

arXiv DOI video
ACC21

Periodic Load Rejection for Floating Offshore Wind Turbines Via Constrained Subspace Predictive Repetitive Control

Liu, Yichao, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In American Control Conference 2021

arXiv DOI

2020

TORQUE20

Fault Detection of the Mooring system in Floating Offshore Wind Turbines based on the Wave-excited Linear Model

Liu, Yichao, Fontanella, Alessandro, Wu, Ping, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In Science of Making Torque from Wind Conference 2020

DOI
IWAI20

Active inference for fault tolerant control of robot manipulators with sensory faults

Pezzato, Corrado, Baioumy, Mohamed, Corbato, Carlos Hernández, Hawes, Nick, Wisse, Martijn, and Ferrari, Riccardo M.G.

In International Workshop on Active Inference 2020

DOI
IFAC20

A Topology-Switching Coalitional Control and Observation Scheme with Stability Guarantees

Chanfreut, Paula, Keijzer, Twan, Ferrari, Riccardo M.G., and Maestre, José Maria

In IFAC World Congress 2020

arXiv DOI video
IFAC20

Fast Adaptive Fault Accommodation in Floating Offshore Wind Turbines via Model-Based Fault Diagnosis and Subspace Predictive Repetitive Control

Liu, Yichao, Wu, Ping, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In IFAC World Congress 2020

arXiv DOI video
ACC20

Adaptive fault accommodation of pitch actuator stuck type of fault in floating offshore wind turbines: a subspace predictive repetitive control approach

Liu, Yichao, Frederik, Joeri, Fontanella, Alessandro, Ferrari, Riccardo M.G., and Wingerden, Jan-Willem

In American Control Conference 2020

DOI
ICRA20

A Novel Adaptive Controller for Robot Manipulators based on Active Inference

Pezzato, Corrado, Ferrari, Riccardo M.G., and Hernández Corbato, Carlos

In International Conference on Robotics and Automation 2020

arXiv video

2019

CDC19

A Sliding Mode Observer Approach for Attack Detection and Estimation in Autonomous Vehicle Platoons using Event Triggered Communication

Keijzer, Twan, and Ferrari, Riccardo M.G.

In Conference on Decision and Control 2019

DOI
NECSYS19

Privacy-Preserving of System Model with Perturbed State Trajectories Using Differential Privacy: With Application to a Supply Chain Network

Nandakumar, Lakshminarayanan, Ferrari, Riccardo M.G., and Keviczky, Tamas

In IFAC Workshop on Distributed Estimation and Control in Networked Systems 2019

DOI

2018

SAFEPROCESS18

Differentially-Private Distributed Fault Diagnosis for Large-Scale Nonlinear Uncertain Systems

Rostampour, Vahab, Ferrari, Riccardo M.G., Teixeira, André M.H., and Keviczky, Tamas

In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2018

DOI
NECSYS18

Attack Detection and Estimation in Cooperative Vehicles Platoons: A Sliding Mode Observer Approach

Jahanshahi, Niloofar, and Ferrari, Riccardo M.G.

In IFAC Workshop on Distributed Estimation and Control in Networked Systems 2018

DOI
ECC18

Detection of Sensor Data Injection Attacks with Multiplicative Watermarking

Teixeira, André M.H., and Ferrari, Riccardo M.G.

2018

DOI

2017

IFAC17

Detection and Isolation of Replay Attacks through Sensor Watermarking

Ferrari, Riccardo M.G., and Teixeira, André M.H.

2017

DOI
IFAC17

A Message Passing Algorithm for Automatic Synthesis of Probabilistic Fault Detectors from Building Automation Ontologies

Ferrari, Riccardo M.G., Baldi, Simone, and Dibowski, Henrik

In IFAC World Congress 2017

DOI
ACC17

Detection and isolation of routing attacks through sensor watermarking

Ferrari, Riccardo M.G., and Teixeira, André M.H.

In American Control Conference 2017

Abs DOI

In networked control systems, leveraging the peculiarities of the cyber-physical domains and their interactions may lead to novel detection and defense mechanisms against malicious cyber-attacks. In this paper, we propose a multiplicative sensor watermarking scheme, where each sensor’s output is separately watermarked by a Single Input Single Output (SISO) filter. Hence, such scheme does not require communication between multiple sensors, but can still lead to detection and isolation of malicious cyber-attacks. In particular, we analyze the benefits of the proposed watermarking scheme for two attack scenarios: the physical sensor re-routing attack and the cyber measurement re-routing one. For each attack scenario, detectability and isolability properties are analyzed with and without the proposed watermarking scheme and we show how the watermarking scheme can be leveraged to detect cyber sensor routing attacks. In order to detect compromised sensors, we design an observer-based detector with a robust adaptive threshold. Additionally, we identify the sensors involved in the re-routing attacks by means of a tailored Recursive Least Squares parameter estimation algorithm. The results are illustrated through a numerical example.
ACC17

A set based probabilistic approach to threshold design for optimal fault detection

Rostampour, Vahab, Ferrari, Riccardo M.G., and Keviczky, Tamas

In American Control Conference 2017

Abs DOI

Traditional deterministic robust fault detection threshold designs, such as the norm-based or limit-checking method, are plagued by high conservativeness, which leads to poor fault detection performance. On one side they are ill-suited at tightly bounding the healthy residuals of uncertain nonlinear systems, as such residuals can take values in arbitrarily shaped, possibly non-convex regions. On the other hand, they must be robust even to worst-case, rare values of the modeling and measurement uncertainties. In order to maximize performance of detection, we propose two innovative ideas. First, we introduce threshold sets, parametrized in a way to bound arbitrarily well the residuals produced in healthy condition by an observer-based residual generator. Secondly, we formulate a chance-constrained cascade optimization problem to determine such a set, leading to optimal detection performance of a given class of faults, while guaranteeing robustness in a probabilistic sense. We then provide a computationally tractable framework by using randomization techniques, and a simulation analysis where a well-known three-tank benchmark system is considered.

2015

SAFEPROCESS15

Optimal Topology for Distributed Fault Detection of Large-scale Systems

Boem, Francesca, Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2015

Abs DOI URL

Abstract The paper deals with the problem of defining the optimal topology for a distributed fault detection architecture for non-linear large-scale systems. A stochastic modelbased framework for diagnosis is formulated. The system structural graph is decomposed into subsystems and each subsystem is monitored by one local diagnoser. It is shown that overlapping of subsystems allows to improve the detectability properties of the monitoring architecture. Based on this theoretical result, an optimal decomposition design method is proposed, able to define the minimum number of detection units needed to guarantee the detectability of certain faults while minimizing the communication costs subject to some computation cost constraints. An algorithmic procedure is presented to solve the proposed optimal decomposition problem. Preliminary simulation results show the potential of the proposed approach.
MMM15

A model for aluminium quenching process optimal setup

Polo, Andrea, Aurora, Claudio, Ferrari, Riccardo M.G., and Cuzzola, Francesco A.

In IFAC Workshop on Mining, Mineral and Metal Processing 2015

Abs DOI URL

The quenching aluminum process is introduced and its complexity associated to setup optimization and closed loop control reviewed. Additionally the basics about a physical mathematical model together with the necessary optimization strategies and some enabling optimization technologies are here summarized.
MMM15

An Algebraic Approach to Modeling Distributed Multiphysics Problems: the case of a DRI Reactor

Ferrari, Riccardo M.G., Boem, Francesca, and Parisini, Thomas

In IFAC Workshop on Mining, Mineral and Metal Processing 2015

Abs DOI URL

This paper deals with the problem of modelling a chemical reactor for the Direct Reduction of Iron ore (DRI). Such a process is being increasingly promoted as a more viable alternative to the classic Blast Furnace for the production of iron from raw minerals. Due to the inherent complexity of the process and the reactor itself, its effective monitoring and control requires advanced mathematical models containing distributed-parameter components. While classical approaches such as Finite Element or Finite Differences are still reasonable options, for accuracy and computational efficiency reasons, an algebraic approach is proposed. A full multi-physical, albeit one-dimensional model is addressed and its accuracy is analysed.

2013

ECC13

An algebraic approach for robust fault detection of input-output elastodynamic distributed parameter systems

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In European Control Conference 2013

Abs DOI URL

This paper deals with the problem of designing a robust fault detection methodology for a class of input-output, uncertain dynamical distributed parameter systems, namely mechanical elastodynamic systems, which are representative of a whole class of problems related to on-line health monitoring of mechanical and civil engineering structures. The proposed approach does not require full state measurements and is robust to measuring, modeling and numerical errors, thanks to a time varying detection threshold. In order to avoid the problems associated with classical discretization techniques for distributed parameter systems, which can lead to numerical errors difficult to bound a priori, and thus higher thresholds, a suitable structure-preserving algebraic approach, called Cell Method, will be employed. This method consists in writing the equations of a distributed parameter system directly in discrete form, avoiding the usual discretization process and leading to a symplectic, that is energy preserving, numerical scheme.
ACC13

Distributed fault detection for uncertain nonlinear systems: A network delay compensation strategy

Boem, Francesca, Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In American Control Conference 2013

Abs DOI

This paper proposes a delay compensation strategy for a distributed fault detection architecture, allowing to manage delays and packet losses in the communication network between the Local Fault Diagnosers. A novel consensus-based estimator with time-varying weights is introduced, permitting to improve detectability in the case of variables shared among more than one subsystem. In the consensus protocol, at each step each agent uses only the information given by the agent and the communication link which are more reliable at that time. The convergence of the proposed estimator is demonstrated and analytical conditions for detectability are derived.

2012

SAFEPROCESS12

Distributed fault diagnosis for input-output continuous-time nonlinear systems

Boem, Francesca, Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2012

Abs DOI URL

In this paper, new results on distributed fault diagnosis of continuous–time nonlinear systems with partial state measurements are proposed. Following an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical systems is described as the interconnection of several subsystems. Each subsystem is monitored by its own Local Fault Diagnoser, based on a set of local estimators. A consensus-based protocol is used to improve the detectability and the isolability of faults affecting variables shared among different subsystems because of the overlapping decomposition. A sufficient condition assuring the convergence of the estimation errors is derived. Time-varying threshold functions guaranteeing no false-positive alarms and theoretical results containing detectability and isolability conditions

2011

CDC11

A distributed fault detection methodology for a class of large-scale uncertain input-output discrete-time nonlinear systems

Boem, Francesca, Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In Conference on Decision and Control 2011

Abs DOI

This paper extends very recent results on a distributed fault diagnosis methodology for nonlinear uncertain large-scale discrete-time dynamical systems to the case of partial state measurement. The large scale system being monitored is modeled, following a divide et impera approach, as the interconnection of several subsystems that are allowed to overlap sharing some state components. Each subsystem has its own Local Fault Diagnoser: the local detection is based on the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. A consensus-based estimator is used in order to improve the detectability of faults affecting variables shared among different subsystems. Time-varying threshold functions guaranteeing no false-positive alarms and analytical fault detectability sufficient conditions are presented as well.
IFAC11

Fault Detection and Isolation of the Wind Turbine Benchmark: an Estimation-based Approach

Zhang, Xiaodong, Zhang, Qi, Zhao, Songling, Ferrari, Riccardo M.G., Polycarpou, Marios M., and Parisini, Thomas

In IFAC World Congress 2011

DOI

2010

CDC10

Distributed fault diagnosis of large-scale discrete-time nonlinear systems: New results on the isolation problem

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In Conference on Decision and Control 2010

Abs DOI

This paper deals with the problem of designing a distributed fault detection and isolation methodology for nonlinear discrete-time dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem, a Local Fault Diagnoser is designed, based on the measured local state of the subsystem as well as the transmitted variables of neighboring states that define the subsystem interconnections. The local diagnostic decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptive approximation of the interconnection with neighboring subsystems. A Global Fault Diagnoser is introduced to isolate distributed faults that cannot be isolated locally. The use of a specially-designed consensus-based estimator is proposed in order to improve the detectability and isolability of faults affecting variables shared among different subsystems. Simulation results are utilized to illustrate of the effectiveness of the proposed distributed fault diagnosis methodology.

2008

ACC08

A robust fault detection and isolation scheme for a class of uncertain input-output discrete-time nonlinear systems

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In American Control Conference 2008

Abs DOI

This paper extends very recent results on discrete- time nonlinear fault detection and isolation to the case of discrete-time nonlinear systems with unstructured modeling uncertainty and partial state measurement. The fault diagnosis architecture consists of a fault detection and approximation estimator and a bank of fault isolation estimators, each corresponding to a particular type of fault. A time-varying threshold that guarantees no false-positive alarms and fault detectability conditions are derived analytically. For the fault isolation scheme, we design adaptive residual thresholds associated with each isolation estimator and obtain sufficient conditions for fault isolability. To illustrate the theoretical results, a simulation example based on a input-output discrete-time version of the three-tank benchmark problem is presented.

2007

CDC07

A fault detection and isolation scheme for nonlinear uncertain discrete-time sytems

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In Conference on Decision and Control 2007

Abs DOI

This paper presents a fault detection and isolation scheme for abrupt and incipient faults in nonlinear uncertain discrete-time systems. The proposed fault diagnosis architecture consists of the fault detection and approximation estimator and a bank of fault isolation estimators, each corresponding to a particular type of fault. A time-varying threshold that guarantees no false-positive alarms and fault detectability conditions is derived analytically. For the fault isolation scheme, we design adaptive residual thresholds associated with each isolation estimator and obtain sufficient conditions for fault isolability. To illustrate the theoretical results, a simulation example based on a discrete-time version of the three-tank problem is presented.
ACC07

Distributed fault diagnosis with overlapping decompositions and consensus filters

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In American Control Conference 2007

Abs DOI

This paper deals with the problem of building a distributed fault detection architecture for large-scale dynamical systems that are modelled as the interconnection of several subsystems. The subsystems are allowed to overlap, thus sharing some state components. For each subsystem a local fault detector is built, such that it can measure the local state of its subsystem as well as receive through communication links a measure of the neighboring states. The local detection decision is made on the basis of the knowledge of the local subsystem dynamic model and of an adaptively learned approximation of the interconnection with neighbouring subsystems. The use of a specially-designed consensus filter is proposed in order to improve the capability of the diagnosers to detect faults affecting variables shared among different subsystems. Simulation results give an evidence of the effectiveness of the proposed fault detection scheme and of the use of overlapping decompositions and consensus filters.

2006

ISIC06

A fault detection scheme for distributed nonlinear uncertain systems

Ferrari, Riccardo M.G., Parisini, Thomas, and Polycarpou, Marios M.

In IEEE International Symposium on Intelligent Control 2006

Abs DOI

This paper considers the problem of designing a fault detection scheme for a distributed nonlinear dynamic system. A network of distributed estimators is constructed where an adaptive estimator based on an on-line neural approximation model is embedded into each estimation agent. The local detection decision is made on the basis of the knowledge of the local dynamic model and on an on-line-learned approximation of the dynamic influence of the neighboring sub-systems. The stability of the adaptive estimation scheme is rigorously investigated and sufficient fault detectability conditions are also proposed. Simulation results are finally provided to demonstrate the effectiveness of the proposed architecture and methodology

other

2015

AISTECH15

Q-VID: a revolutionary approach to vision-based measurements and control

Visentini, Ingrid, Ferrari, Riccardo M.G., and Ardesi, Alessandro

In AISTech 2015 — The Iron and Steel Technology Conference and Exposition 2015

Abs DOI URL

Vision-based applications are emerging as a new trend in the steelmaking industry, being exploited in a wide range of situations due to their advantageous tradeoff between deployment flexibility, cost and quality. Here we present a new instrumentation systems family , named Q-V I D , capable of accurate measurements of hot and cold products by groundbreaking and robust computer vision techniques, specifically designed to fit accuracy and real-time constraints. We will describe its convenience with respect to more traditional, application-specific optical measuring approaches, and provide three real-world examples: * an overview of a full system with tight deployment constraints, to show how the Q-VID modules can be deployed in out-of-reach plant infrastructure in a distributed fashion with no particular efforts; * a case study for standard cameras chassis, to guarantee the components operability even in harsh conditions, such as high temperatures, dust and vibrations; * a software framework, common to all the Q-VID products, which shapes several applications, and allows to meet the expected performance and reliability.
AISTECH15

Q–Robot ROLL - Vision–based automatic deburring robot for cold billets

Ferrari, Riccardo M.G., Romano, Fulvio, and Visentini, Ingrid

In The Iron and Steel Technology Conference and Exposition (AISTech) 2015 2015

Abs URL

Torch and saw cutting may lead to burrs in rolled products (slabs, blooms, billets) at the trimmed edge, which have to be cleared to guarantee defect-free further processing. A fully automated deburring robot is here proposed, which uses 3D stereo vision and machine learning techniques to localize the billet profile with 6 DoF. The major topic this machine has to face off is the flexibility. One standard, though customizable, machine has to deburr a large amount of different shapes (round, square, etc.), randomly positioned and up to a productivity of 90 t/h.

2009

COMSOL

The Acoustoelastic Effect: EMAT Excitation and Reception of Lamb Waves in Pre-Stressed Metal Sheets

Ferrari, Riccardo M.G.

In Comsol Conference 2009 2009

PDF Slides Website URL

2004

THESIS

Il Fenomeno Acustoelastico in Fogli Metallici Sottoposti a Tensione: Studio Teorico, Determinazione Sperimentale e Simulazione Mediante il Metodo delle Celle

Ferrari, Riccardo M.G.

2004

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