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This paper presents an attitude active fault tolerant control for a satellite simulated model. The proposed fault tolerant controller consists of two main parts: a nominal controller, designed assuming that the system is healthy, and a fault diagnosis module which aims at detecting, isolating and estimating the fault affecting the system. The fault diagnosis module is designed by using the non-linear geometric approach tool which allows to obtain detection residuals and estimation filter decoupled from the aerodynamic disturbance representing the main uncertainty in low Earth orbits. The simulation results show the advantages, in terms of attitude tracking accuracy, obtainable when implementing the proposed method.

This paper addresses the development of an active fault tolerant control scheme for avionic systems. The methodology is applied to an aircraft longitudinal autopilot taking into account possible faults on the aircraft actuators. The key feature of the proposed control relies on its active characteristics, as the fault diagnosis strategy is based on a robust estimate of the fault signals that are compensated. The design method uses an intelligent data–driven scheme via a fuzzy modelling and identification procedure, which derives these adaptive filters with disturbance decoupling features. The work shows that these fault estimates can be used for fault accommodation. In particular, the fuzzy approach proposed in the paper provides the reconstruction of the fault signals that are decoupled from the wind components, and thus applied to the aircraft system. The proposed solutions provide interesting robustness features that are analysed by using a high–fidelity simulator, which is able to include different operating points and realistic actuator faults, turbulence, measurement errors, and the model–reality mismatch.

This paper suggests a model-free framework for Fault Detection and Isolation (FDI) of satellite reaction wheels for the first time. The proposed FDI method is based on multi-classifier fusion with diverse learning algorithms and configured in a parallel form where a unique module simultaneously performs both detection and isolation tasks. In other words, a multi-classifier-based arrangement is presented on the basis of Mixed Learning strategy where four classic and well-practised classification schemes including Random Forest, Support Vector Machine, Partial Least Square, and Naive Bayes are incorporated into FDI module in order to make a decision on the occurrence of a fault and its location. Extensive simulation results with a high-fidelity nonlinear spacecraft simulator considering gyroscopic effects, measurement noise, and exogenous aerodynamic disturbance signals show that the proposed FDI scheme can cope with faults affecting reaction wheel torques and obtain promising FDI performances in most of the designed scenarios.

This paper presents a novel scheme for diagnosis of faults affecting sensors that measure the satellite attitude, body angular velocity, flywheel spin rates, and defects in control torques from reaction wheel motors. The proposed methodology uses adaptive observers to provide fault estimates that aid detection, isolation, and estimation of possible actuator and sensor faults. The adaptive observers do not need a priori information about fault internal models. A nonlinear geometric approach is used to avoid that aerodynamic disturbance torques have unwanted influence on the fault estimates. An augmented high-fidelity spacecraft model is exploited during design and validation to replicate faults. This simulation model includes disturbance torques as experienced in low Earth orbits. This paper includes an analysis to assess robustness properties of the method with respect to parameter uncertainties and disturbances. The results document the efficacy of the suggested methodology.

This paper addresses the fault diagnosis problem of sensors of an aeronautical system based on GNSS/IMU. The model considered is a rigid body with 6DoF. Considering its linearized kinematic model, the proposed FDI scheme is based on a generalized bank of observers (GBO) designed with the UIO theory. The fault scenario is composed by multiple but not concurrent faults without any hypothesis on the kind of fault. The structural conditions for fault diagnosability are analytically demonstrated.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this chapter is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator represent the instruments for assessing the main aspects of the developed control methodologies.

In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. This paper addresses the design of an active fault tolerant control scheme that is applied to a wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. Note that, due to the structure of the system and its control strategy, it can be considered as a fault tolerant cooperative control problem of an autonomous plant. The controller accommodation scheme provides the on-line estimate of the fault signals generated by nonlinear filters exploiting the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a data-driven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. This feature of the work, followed by the simpler solution relying on a data-driven approach, can represent the key point when on-line implementations are considered for a viable application of the proposed scheme.

This paper presents an Attitude Active Fault Tolerant Control for satellite. The proposed fault tolerant controller is constituted by two main parts: a nominal controller, designed assuming that the system is healthy, and a fault diagnosis module which is in charge of detecting, isolating and estimating the fault affecting the system. The fault diagnosis module is designed by using the Non Linear Geometric Approach which allows for the design of detection residuals and estimation filter decoupled from the aerodynamic disturbance which represents the main uncertainty in low Earth orbits. The simulation results show the benefits, in terms of attitude tracking accuracy, arising when adopting the proposed method.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

This paper presents a novel scheme for the detection and isolation of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite momentum wheels. Thanks to the jointly use of the singular perturbations theory and nonlinear geometric approach, a novel fault detection and isolation system has been developed to accurately detect and isolate faults occurring on all the considered actuators and sensors. Simulation results are based on a detailed nonlinear satellite model with embedded disturbance description. The results document the effectiveness of the proposed fault detection and isolation scheme.

SUMMARY This paper describes the design of an active fault-tolerant control scheme that is applied to the actuator of a wind turbine benchmark. The methodology is based on adaptive filters obtained via the nonlinear geometric approach, which allows to obtain interesting decoupling property with respect to uncertainty affecting the wind turbine system. The controller accommodation scheme exploits the on-line estimate of the actuator fault signal generated by the adaptive filters. The nonlinearity of the wind turbine model is described by the mapping to the power conversion ratio from tip-speed ratio and blade pitch angles. This mapping represents the aerodynamic uncertainty, and usually is not known in analytical form, but in general represented by approximated two-dimensional maps (i.e. look-up tables). Therefore, this paper suggests a scheme to estimate this power conversion ratio in an analytical form by means of a two-dimensional polynomial, which is subsequently used for designing the active fault-tolerant control scheme. The wind turbine power generating unit of a grid is considered as a benchmark to show the design procedure, including the aspects of the nonlinear disturbance decoupling method, as well as the viability of the proposed approach. Extensive simulations of the benchmark process are practical tools for assessing experimentally the features of the developed actuator fault-tolerant control scheme, in the presence of modelling and measurement errors. Comparisons with different fault-tolerant schemes serve to highlight the advantages and drawbacks of the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

SUMMARY This paper presents new results regarding the development of a supervision scheme for a nonlinear satellite model. The main issue concerns the handling of frequency faults affecting the reaction wheels of a spacecraft attitude control system, that is, how to detect and isolate faults, how to determine the different frequencies characterising these faults through spectral analysis and lastly, how to prevent propagation into failures with potential mission abortion as a consequence. Thus, this work investigates the design of a scheme for fault detection, isolation and control reconfiguration applied to the reaction wheels of a spacecraft attitude control, based on the satellite model. This scheme is classifiable as active fault tolerant control. As the study focuses on a general satellite nonlinear model, where aerodynamic and gravitational disturbances, as well as measurement errors, are present, the robustness of the suggested strategy is achieved by exploiting an explicit disturbance decoupling method via a nonlinear geometric approach. To achieve accurate fault diagnosis, aerodynamic disturbance decoupling represents the key point because the aerodynamic model is often uncertain. Moreover, an improvement of the nonlinear geometric approach is presented, to realise both aerodynamic and manoeuvre decoupled fault diagnosis. To the best authors’ knowledge, this is the first works presenting a methodology for frequency fault diagnosis, which is based on the nonlinear geometric approach for fault and disturbance decoupling. The obtained results demonstrate that the proposed methodology can achieve better performances with respect to traditional fault detection and isolation schemes. Copyright © 2013 John Wiley & Sons, Ltd.

This paper examines the problem of designing a module for the detection and isolation of faults affecting quadrotors' actuation subsystem. Furthermore, differently from other diagnosis scheme available in literature, in this paper (not necessary small) environmental disturbances are also taken into account. In particular, this paper proposes a new two-steps strategy, based on the NonLinear Geometric Approach, for fault detection and isolation: in the first step, the application of the NonLinear Geometric Approach leads to coordinate changes able to highlight new systems affected by the wind and by a part of all possible faults; the second step exploits wind estimators designed on the equations of the new systems found at the end of the step one. These estimators are combined to generate a residuals set structured for fault detection and isolation. Simulation results demonstrate and validate the performance and the capabilities of the proposed fault detection and isolation algorithm.

In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. Therefore, this paper addresses the design of an active fault tolerant control scheme that is applied to a small wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. The controller accommodation scheme exploits the on-line estimate of the fault signals generated by nonlinear filters using the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a data-driven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. The wind farm benchmark is considered to validate the performances of the suggested scheme in the presence of different fault conditions, modelling and measurement errors.

This work presents two active fault tolerant control systems for aerospace applications. The former case study regards an aircraft longitudinal autopilot and the latter one a satellite attitude control system, both in case of faults affecting the actuators. The main features of the presented active fault tolerant control schemes are the fault detection and diagnosis module and its design technique, i.e. the nonlinear geometric approach. Such approach allows, using adaptive filters in the fault detection and diagnosis module, fault detection, isolation and estimation. The fault estimates, obtained by different methods including recursive least squares and neural network, are exploited by a controller reconfiguration mechanism. In particular, by means of the nonlinear geometric approach, relying on nonlinear differential algebra, it is possible to obtain fault estimates decoupled from wind components in case of aircraft and aerodynamic disturbances in case of spacecraft, thus giving to the overall control system very good robustness properties and performances. The effectiveness of the designed solutions is shown by means of high fidelity simulators, in different flight conditions and in the presence of faults on actuators, turbulence, measurement noise, and modelling errors.

This paper proposes an active fault tolerant control scheme to cope with faults or failures affecting the flywheel spin rate sensors or satellite reaction wheel motors. The active fault tolerant control system consists of a fault detection and diagnosis module along with a control allocationand fault accommodation module directly exploiting the on-line fault estimates. The use of the nonlinear geometric approach and radial basis function neural networks allows to obtain a precise fault isolation, independently from the knowledge of aerodynamic disturbance parameters, and to design generalised estimation filters, which do not need a priori information about the internal model of the signal to be estimated. Theadaptive control allocation and sensor fault accommodation can handle both temporal faults and failures. Simulation results illustrate the convincing fault correction and attitude control performances of the proposed system.

This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filters that do not need a priori information about the fault internal models. Simulation results are based on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

This paper presents new results regarding the development of a fault detection and isolation scheme for a nonlinear satellite model. The main issue concerns the handling of frequency faults affecting the reaction wheels of a spacecraft attitude control system, i.e. how to detect and isolate faults, and lastly how to determine the different frequencies characterising these faults through spectral analysis. As the study focusses on a general satellite nonlinear model, where aerodynamic and gravitational disturbances, as well as measurement errors are present, the robustness of the suggested strategy is achieved by exploiting an explicit disturbance decoupling method via a nonlinear geometric approach. In order to achieve accurate fault diagnosis, aerodynamic disturbance decoupling represents a key point, since the aerodynamic model is often uncertain. Moreover, an improvement of the nonlinear geometric approach is presented, in order to realise an aerodynamic decoupled fault diagnosis with normalized fault sensitivity. To the best authors? knowledge, this is the first works presenting a methodology for frequency fault diagnosis which is based on the nonlinear geometric approach for the fault and disturbance decoupling. The obtained results demonstrate that the proposed methodology can achieve better performances with respect to traditional fault detection and isolation schemes.

In order to improve reliability of wind turbines, it is important to detect and isolate faults as fast as possible, and handle them in an optimal way. An important component in modern wind turbines is the converter, which for a wind turbine control point–of–view normally provides the torque acting on the wind turbine generator, as well as measurement of this torque. In this work, a diagnosis strategy based on fuzzy prototypes is presented, in order to detect these faults in the converter, and isolate them either to be an actuator or a sensor fault. The fuzzy system is used since the model under investigation is nonlinear, whilst the wind speed measurement is highly noisy, due to the turbulence around the rotor plane. The fuzzy system consists of a set of piecewise affine Takagi–Sugeno models, which are identified from the noisy measurements acquired from the simulated wind turbine. The fault detection and isolation strategy is thus designed based on these fuzzy models. The wind turbine simulator is finally used to validate the achieved performances of the suggested fault detection and isolation scheme.

The main contribution of this paper is the description and application of a comprehensive set of methodologies for fault detection and isolation (FDI) of aircraft sensors. In particular, a new nonlinear geometric approach (NLGA) and an efficient linear polynomial method (PM) are presented and compared, together with simulation results obtained from a commercial aircraft model. Adaptive filters with disturbance decoupling for fault identification are designed via the developed NLGA-based method. On the other hand, the FDI scheme based on linear PM exploits a disturbance decoupling technique in connection with a linear dynamic filter design procedure. The FDI strategies are applied to the aircraft simulator data in a flight condition characterised by tight–coupled longitudinal and lateral dynamics. Moreover, in order to analyse robustness and reliability properties of the two FDI schemes, extensive simulations are performed in the presence of turbulence, measurement noise and modelling errors.

This paper focuses on the problem of nonlinear fault detection, diagnosis, and fault tolerant control for a satellite model through the construction of disturbance decoupled adaptive filters designed via the nonlinear geometric approach. Each filter of the bank is sensitive to all but one fault, and decoupled from aerodynamic disturbances. In order to fulfil the requirements of the proposed mission, the disturbance decoupling represents the key point, since the aerodynamic model is often uncertain. To the authors' knowledge, this is the first work presenting the solution of the aerodynamic disturbance decoupling problem. The controller reconfiguration logic exploits a further control loop depending on the on-line estimate of the fault signals. Simulations results for the case of a nonlinear satellite model with aerodynamic, gravitational, magnetic, and solar pressure disturbances are presented with reference to a pointing mission. The suggested approach allows to achieve better performances with respect to control schemes simply designed to be robust with respect to the considered disturbance contributions.

The paper addresses the problem of the design and the application of nonlinear filtering techniques for the detection and isolation of aircraft model actuator faults. In particular, the main contribution of this work is to combine the particle filtering technique with the nonlinear geometric approach for disturbance decoupled residual functions. By using aircraft simulator data in a flight condition characterised by tight–coupled longitudinal and lateral dynamics, the proposed fault diagnosis schemes are validated. Finally, in order to analyse and compare the performance capabilities of the developed fault diagnosis strategies, extensive simulations are performed in the presence of turbulence, measurement noise and modelling errors.

This paper addresses the problem of detection and isolation of actuator faults for a general aviation aircraft, where the nonlinear simulation aircraft model embeds the effects of wind gusts, atmospheric turbulence and measurement errors. The approach is based on the recently introduced NonLinear Geometric Approach (NLGA) to the Fault Detection and Isolation (FDI) problem. The actuators FDI is performed in a coordinated turn, hence with coupled longitudinal and lateral–directional dynamics. This work represents the first complete NLGA FDI scheme for a general aviation aircraft in a condition with tight–coupled dynamics. The results obtained in the simulation of the faulty behaviour of a nonlinear Piper PA30 aircraft are finally reported.

In order to improve the safety, the reliability, and the efficiency of offshore wind park installations, thus avoiding expensive unplanned maintenance, the accommodation of faults in their earlier occurrence is fundamental. Therefore, the main contribution of this paper consists of the development of a fault tolerant control scheme by means of a viable approach. In particular, a data-driven strategy based on fuzzy logic is exploited for deriving the model of the required controller. Fuzzy theory is exploited here since it is able to approximate easily unknown nonlinear models and manage noisy measurements. Moreover, the controller fuzzy prototype is directly identified from the wind farm measurements, and it provides the straightforward achievement of the fault tolerant control scheme. In general, an analytic approach, where the system nonlinearity is explicitly taken into account, could require more complex design methodologies. This aspect of the work, followed by the simpler solution relying on fuzzy rules, represents the key point when on-line implementations are considered of the proposed control scheme. To highlight the potential of the proposed fault tolerant control algorithm in real applications, a hardware-in-the-loop test facility representing a realistic offshore wind farm installation is considered to analyse the digital implementation of the designed controller. The results have shown that the developed scheme maintains desired performances, thus validating its reliability also in real-time implementations.

This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank of residual filters organized in a generalised scheme, followed by a fault estimation module consisting of a bank of adaptive estimation filters. The residuals are decoupled from aerodynamic disturbances thanks to the Nonlinear Geometric Approach. The use of Radial Basis Function Neural Networks is shown to allow design of generalised fault estimation filters, which do not need a priori information about the faults internal model. Simulation results with a detailed nonlinear spacecraft model, which includes disturbances, show that the proposed diagnosis scheme can deal with faults affecting both reaction wheel torques and flywheel spin rate measurements, and obtain precise fault isolation as well as accurate fault estimates.

In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. Therefore, this paper addresses the design of an active fault tolerant control scheme that is applied to a small wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions among the wind turbines in the wind farm. The controller accommodation scheme relies on the on-line estimate of the fault signals generated by nonlinear filters designed via the nonlinear geometric approach. In this way, these estimates are decoupled from both the model uncertainty and the interactions among the turbines. This paper proposes also a data-driven scheme to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. In general, purely nonlinear analytic approaches, where the system nonlinearity and disturbance decoupling properties are explicitly considered, could require complex design strategies. Therefore, this simpler solution relying on a data-driven approach can represent the key point when on-line implementations are considered for the viable application of the proposed scheme. The wind farm benchmark is considered to validate the performances of the suggested scheme in the presence of different fault conditions, modelling and measurement errors. The methodology proposed is also compared with respect to two different fault tolerant control strategies.

This paper addresses the Fault Detection and Isolation (FDI) of the problem of multiple non-concurrent faults on actuator and sensor of quadrotors, which is described by a nonlinear model. To the best of authors' knowledge, this paper provides, for the first time, a solution to the FDI problem for quadrotors affected by both actuator and sensor faults. The literature proposes FDI schemes dedicated only to actuators or only to sensors, indeed. The Non Linear Geometric Approach is based on the mathematical model of the system and allows to determine detection filters sensitive to a selected set of fault and structurally decoupled from the other faults (out of the selected set) thus providing the base for the fault isolation. Several simulation trials have been performed and the analysis of the results proves that the developed diagnostic system represents an effective solution to the problem of multiple actuator and sensor fault detection and isolation for quadrotors.

The paper presents a new method for the estimation of the electric parameters of induction motors (IMs). During the identification process the rotor flux is also estimated. The procedure relies on standstill tests performed with a standard drive architecture, hence, it is suitable for self-commissioning drives. The identification scheme is based on the model reference adaptive system (MRAS) approach. A novel parallel adaptive observer (PAO) has been designed, starting from the series-parallel Kreisselmeier observer. The most interesting features of the proposed method are the following: 1) rapidity and accuracy of the identification process; 2) low-computational burden; 3)excellent noise rejection, thanks to the adopted parallel structure; 4) avoidance of incorrect parameter estimation due to magnetic saturation phenomena, thanks to recursive rotor flux monitoring. The performances of the new scheme are shown by means of simulation and experimental tests. The estimation results are validated by comparison with a powerful batch nonlinear least square (NLS) method and by evaluating the steady-state mechanical curve of the IM used in the tests.

In this paper the frequency domain version of the Frisch identification scheme is applied to identify parameters of the continuous-time model of an induction motor. A formulation of the identification problem in the errors-in-variables framework is given, in particular this formulation allows handling of periodic signals affected by noises with stochastic properties. A new approach, based on Bilinear Matrix Inequalities, is introduced to estimate noise variances of measured signals in the Frisch scheme. Simulations and experimental results are reported to show the properties of the proposed approach.

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system.

This contribution describes the application of differential geometry and nonlinear systems analysis to the estimation of friction effects in a class of mechanical systems. The proposed methodology, that has been developed for the more general problem of fault detection and diagnosis, relies on adaptive filters designed with a nonlinear geometric approach to obtain the disturbance de-coupling property. The classical model of an inverted pendulum on a cart is considered as an application example, in order to highlight the complete design procedure, including the mathematical aspects of the disturbance de-coupling method as well as the feasibility and the efficiency of the approach. Thanks to accurate estimation, friction effects can also be compensated by means of a controller designed to inject the on-line estimate of friction force to the control action calculated by classical linear state feedback. This strategy, which belongs to the class of so-called Active Fault-Tolerant Control Schemes, allows to maintain existing controllers and enhance their performance by introducing an adaptive estimator of unmodeled friction forces.

In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution.

This paper proposes a solution to the aircraft control problem during landing phases in presence of microburst wind shear. This work addresses the design of an adaptive controller which, exploiting the wind estimation provided by suitable filters, improves the flight safety and is able to accomplish the final approach phase even in presence of wind shear. The adaptive controller has been designed by using a backstepping approach and a feedback linearization for time–varying systems. The wind components are estimated by adaptive filters designed by using the tools of both the nonlinear geometric approach and the radial basis function neural network.

This work shows how to use a differential geometry tool to design a novel nonlinear active fault tolerant flight control system for aircraft. The proposed control scheme consists of two main subsystems: a controller, which is designed for the nominal plant, and a fault detection and diagnosis module, which provides fault estimation. A further feedback loop exploits the fault estimation to accommodate faults affecting the system. The estimate convergence and the stability of the active fault tolerant flight controller are theoretically proved. Finally, high fidelity simulations show the effectiveness of the scheme.

A new application of a Kalman-Like robust filtering scheme to Avionics Dead Reckoning is presented. Inertial and air data measurements are used in order to reconstruct the attitude and the body frame velocity of a general aviation aircraft. The proposed estimation algorithm is based on an afiine representation of the non linear model of the system, leading to a Kalman filter gain optimizing procedure under a set of Linear Matrix Inequality constraints. The resulting procedure fulfills the standard accuracy requirements for large set. of flight condition, and exhibits better robustness characteristics with respect to the model uncertainty than the classic and widely used Extended Kalman Filter.

This paper describes the design of a robust fault diagnosis scheme that is applied to the actuators of a wind turbine benchmark. The methodology is based on adaptive filters obtained via a nonlinear geometric approach, which allows to obtain interesting decoupling property with respect to uncertainty affecting the wind turbine system. The residual generation scheme exploits the on-line estimation of the actuator fault signal generated by the adaptive filters. The nonlinearity of the wind turbine model is described by the mapping to the power conversion ratio from tip-speed ratio and blade pitch angles, usually not known in analytical form. The wind turbine unit is considered as benchmark to show the design procedure, including the aspects of the nonlinear disturbance decoupling method, as well as the viability of the proposed approach. Extensive simulations of the benchmark process are practical tools for assessing experimentally the features of the developed actuator fault diagnosis scheme, in the presence of modelling and measurement errors.

Blind identification is a significant problem in many communication systems. A new approach to blind estimation and input deconvolution is developed in this paper. By using the structural shift properties of the generalized Sylvester matrix of the system, an estimate of the covariance matrix of the unknown source is obtained and a suitable identification criterion for the estimation of the channel coefficients is introduced. A reconstruction of the input sequence is then obtained by means of a standard LS equalizer.

This paper proposes a novel satellite attitude Active Fault Tolerant Control System. The Active Fault Tolerant Control System is based on a Fault Detection and Diagnosis module providing fault estimates decoupled from the aerodynamic disturbance thanks to the application of the NonLinear Geometric Approach. The diagnosis module is a bank of adaptive filters based on a Radial Basis Function Neural Network. The controller reconfiguration exploits directly the on-line estimates of the fault signals. To the authors' knowledge, the joint use of the NonLinear Geometric Approach and Radial Basis Function Neural Network is not present in literature. The use of a Radial Basis Function Neural Network allows to design generalized fault estimation filters which do not need any a priori information about the fault internal model. The use of the NonLinear Geometric Approach allows to obtain very accurate fault estimates, independently from the knowledge of the aerodynamic disturbance parameters. Extensive simulation results based on a detailed nonlinear satellite attitude model embedding disturbance description are given. The obtained results highlight that the proposed satellite Active Fault Tolerant Control System can deal with reaction wheel faults of different types (i.e. step faults, ramp faults, sinusoidal faults) thus obtaining good attitude control performances.

Wind turbines are complex dynamic systems forced by stochastic wind disturbances, as well as gravitational, centrifugal, and gyroscopic loads. Since their aerodynamics are nonlinear, wind turbine modelling is thus challenging. Moreover, accurate models should contain many degrees of freedom to capture the most important dynamic effects. Therefore, the design of control algorithms for wind turbines should account for these complexities. However, these algorithms must capture the most important turbine dynamics without being too complex and unwieldy. The main purpose of this study is thus to give two examples of viable and practical designs of control schemes with application to a wind turbine prototype model. Extensive simulations on the benchmark process and Monte-Carlo analysis are the tools for assessing experimentally the main features of the proposed control schemes, in the presence of modelling and measurement errors. These developed control methods are also compared with other different approaches, in order to evaluate advantages and drawbacks of the considered solutions. Finally, Hardware-In-the-Loop simulations serve to highlight the potential application of the proposed control strategies to real wind turbines.

This paper presents a scheme for accommodating faults in the blade pitch, rotor speed and generator torque control sensors of a wind turbine nonlinear system. These measured values are important both for the controller performance as well as the supervisory module of the wind turbine. The proposed control accommodation scheme is based on identified nonlinear filters, which can be also exploited to diagnose these faults. In particular, a nonlinear dynamic system estimation scheme is proposed to obtain these disturbance decoupled nonlinear filters, which provide the on-line estimate of the fault signals. The scheme is tested on a high-fidelity wind turbine simulator, in the presence of disturbance and measurement errors, along nominal operating conditions, including also different realistic fault situations. Tests on this simulated model show the the reliability features of the proposed active fault tolerant control scheme.

This paper addresses the development of an active fault tolerant control scheme that is applied to a wind turbine simulated benchmark. The proposed methodology is based on a comprehensive scheme relying on adaptive controllers designed by means of the on–line identification of the system model under diagnosis. In this way, the controller reconfiguration mechanism exploits an adaptive regulator implementation, depending on the on–line estimate of system model. One of the advantages of this strategy is that, for example, the original structure of logic–based switching digital controller scheme already implemented for the wind turbine benchmark can be almost preserved. The active fault tolerant control scheme is therefore applied to a wind turbine simulated benchmark, in the presence of disturbance and measurement errors, along nominal operating conditions, including also different realistic fault situations. The achieved results in both fault–free and faulty conditions serve to show the enhancement of the control performances, and the fault accommodation features.

This paper proposes a fuzzy modelling and identification approach oriented to the design of a fault tolerant fuzzy controller for regulating both the pitch angle and the reference torque of a wind turbine benchmark. This strategy has been suggested for enhancing the regulator design that could represent an alternative to the standard switching controller, already implemented in the wind turbine test system. The controller project requires the knowledge of a dynamic model of the wind turbine, which is achieved by means of a fuzzy modelling and identification scheme. On the other hand, the proposed fuzzy controller structure is straightforward and easy to implement with respect to different strategies proposed in literature. Moreover, by exploiting the fuzzy identification procedure, the proposed strategy is also able to provide a fault tolerant controller. In this way, the fault tolerance properties are thus achieved by using a so called passive scheme. The results obtained with the designed fuzzy controller are compared to those of a switching controller already implemented for the wind turbine benchmark.

Active fault tolerant control of nonlinear systems: The cart-pole exampleThis paper describes the design of fault diagnosis and active fault tolerant control schemes that can be developed for nonlinear systems. The methodology is based on a fault detection and diagnosis procedure relying on adaptive filters designed via the nonlinear geometric approach, which allows obtaining the disturbance de-coupling property. The controller reconfiguration exploits directly the on-line estimate of the fault signal. The classical model of an inverted pendulum on a cart is considered as an application example, in order to highlight the complete design procedure, including the mathematical aspects of the nonlinear disturbance de-coupling method based on the nonlinear differential geometry, as well as the feasibility and efficiency of the proposed approach. Extensive simulations of the benchmark process and Monte Carlo analysis are practical tools for assessing experimentally the robustness and stability properties of the developed fault tolerant control scheme, in the presence of modelling and measurement errors. The fault tolerant control method is also compared with a different approach relying on sliding mode control, in order to evaluate benefits and drawbacks of both techniques. This comparison highlights that the proposed design methodology can constitute a reliable and robust approach for application to real nonlinear processes.

In order to improve reliability of wind turbines, it is important to detect faults in their very early occurrence, and to handle them in an optimal way. This paper focuses on the pitch sensors of the turbine blade system, as they are mainly used for wind turbine control, in order to maximise the power production, and the efficiency of the whole process. On the other hand, as the input-output behaviour of the system under diagnosis is nonlinear, this work suggests a modelling scheme relying on piecewise affine models, whose parameters are identified through the acquired input–output measurements affected by measurement uncertainty. Therefore, these hybrid prototypes are exploited for generating suitable residual signals, which allow the detection and the isolation of the considered sensor faults. This noise rejection scheme is used since the wind turbine measurements are not very reliable, due to the uncertainty of wind speed acting on the wind turbine, and to the turbulence around the rotor plane. A detailed benchmark model simulating the wind turbine where realistic fault conditions can be considered shows the effectiveness of both the identification and fault diagnosis techniques.

This paper presents a novel approach to the longitudinal guidance and control issue for an aircraft in presence of wind shear. The main contribution concerns the adaptive estimation of the wind shear disturbances affecting the aircraft and the development of a control scheme suitable to compensate these effects during a precision approach procedure. The work proposes the design, based on the Nonlinear Geometric Approach, of three adaptive filters providing the estimate of the wind shear disturbance components. These estimates are exploited, in an original way, by a BackStepping based controller, thus resulting in an Adaptive BackStepping Controller. Simulation results, obtained by means of a detailed flight simulator implementing the real wind shear condition which caused the 1975 crash of Eastern Flight 066 at JFK airport and a Montecarlo robustness test, demonstrates the effectiveness of the proposed method.

This paper explains the design method of an innovative active fault tolerant control scheme and the achieved results regarding its application to aerospace nonlinear models. The proposed method keeps the already in–place control and guidance laws and adds a feedback loop that accommodates the fault. The kernel of this active fault tolerant control consists of the fault detection and diagnosis module designed by using the nonlinear geometric approach. Thanks to this approach fault estimates are analytically decoupled from both the other faults and disturbances. The novel active fault tolerant control has been tested by using high fidelity simulators of aircraft and spacecraft systems, whilst the performances show the method robustness with respect to disturbance effects and measurement errors. The results obtained demonstrate how the proposed design methodology could be a successful approach for the reliable design of fault tolerant control schemes in real aircraft and spacecraft applications.

This paper presents preliminary results regarding the development of a supervision scheme for the attitude control system of a nonlinear satellite model. The main issue concerns the handling of faults affecting the reaction wheels, i.e. how to detect and isolate faults, and how to prevent propagation into failures with potential mission loss as a consequence. Thus, this work investigates the design of a scheme for fault detection, isolation and control reconfiguration applied to the reaction wheels of a spacecraft attitude control, based on the satellite model. As the study focuses on a general satellite nonlinear model, where aerodynamic and gravitational disturbances, as well as measurement errors are present, the robustness of the suggested strategy is achieved by exploiting an explicit disturbance de– coupling method. The results obtained demonstrate how the proposed methodology can constitute a successful approach for real application in future spacecraft.

In this paper consideration is given to the problem of determining an optimal estimate of the output of a linear dynamic SISO system from the knowledge of the input-output data corrupted by additive noise. The solution of this problem can be divided into two steps: first. the model of the system and of the noise affecting the data is identified. then a Kalman filter is designed on the basis of this model. Since the result of the identification scheme may be a whole family of models. a comparison between these different systems is analyzed with refcrence to the behavior of the associated Kalman filters. Some silmulation results are finally discussed.

This paper addresses the development of a novel active fault tolerant control scheme. The methodology is based on a fault detection and diagnosis procedure relying on adaptive filters designed via the nonlinear geometric approach. The controller reconfiguration exploits a further control loop, depending on the on-line estimate of the fault signal. One of the advantages of this strategy is that, for example, a structure of logic-based switching controller is not required. The active fault tolerant control scheme is therefore applied to a PA-30 aircraft simulator in several flight conditions, in the presence of actuator faults, turbulence, measurement noise, and modelling errors. The achieved results in faulty conditions show the enhancement of the flying quality, the asymptotic fault accommodation, and the control objective recovery.

In this work, different procedures for sensor Fault Detection and Isolation (FDI) applied to a simulated model of a commercial aircraft are presented. The main point of the paper regards the design of two FDI schemes based on a linear Polynomial Method (PM) and the NonLinear Geometric Approach (NLGA). The obtained results highlight a good trade–off between solution complexity and achieved performances. The FDI schemes are applied to the aircraft model, characterised by tight–coupled longitudinal and lateral dynamics. The properties of the residual generators are experimentally investigated and verified by simulating a general aircraft reference trajectory. The overall performance of the developed FDI schemes are analysed in the presence of turbulence, measurement and model errors. Comparisons with other FDI methods based on Neural Networks (NN) and Unknown Input Kalman Filter (UIKF) are finally reported.