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Traditional model-based control methods are often not applicable in industrial processes given the typical situation that model parameters are unknown, coefficient matrices are difficult to obtain, and system states are unpredictable. Accordingly, an output feedback fault-tolerant control method based on zero-sum game theory and off-policy Q-learning is presented in this study, with the aim of achieving smooth operation and good tracking performance for industrial processes that often contain sensor faults and disturbances. The specific steps are as follows. First, a system tracking error is introduced into the system to realize a novel extended model. Second, by establishing a performance index function and combining it with minimax theory, the fault-tolerant tracking control problem is converted into a zero-sum game problem. The Bellman and Riccati equations can be established after analyzing the relationship between the performance index and value functions. Then, the Q-function is introduced, and an off-policy Q-learning algorithm is combined with the Kronecker product without knowledge of system model parameters to design an optimal controller unbiased to detection noise. Finally, the effectiveness of the algorithm is verified by considering the injection molding process as an example. The experimental results validate that the designed controller demonstrates good control and extends the range of tolerable faults while maintaining good tracking performance. © 2023 Elsevier Ltd

A novel two-mode inverter-based open-winding permanent magnet synchronousmotor drive is proposed in this article. The proposed drive operates in hybrid-inverter mode at low and medium speed to achieve high power quality. The common dc-bus mode is adopted at high speed to fulfill a wider speed range than hybrid-inverter mode. Based on the proposed drive, a modulation strategy for any voltage ratio is proposed, which integrates the instantaneous floating capacitor (FC) voltage into modulation to synthesize the reference voltage vector. The proposed modulation strategy can not only guarantee the FC voltage regulation but also start the drive when the FC voltage is zero and switch the mode of the drive smoothly. Besides, the excellent fault-tolerant capability of the proposed drive is studied under open-phase and switch faults. In the proposed fault-tolerant control, a sinusoidal zero-sequence current is injected to tolerate the open-phase fault in the common dc-bus mode and redundant switching combinations are used to tolerate switch faults in the hybrid-inverter mode. All the claimed advantages have been validated by experimental results.

Dual three-phase permanent magnet synchronous machine (DTP-PMSM) has received extensive interests due to its inherent fault-tolerant capability. However, conventional single-virtual-vector-based predictive current control (SVV-PCC) for DTP-PMSM with open-phase fault (OPF) presents poor steady-state performance and high computational burden. To address these issues, a multi-virtual-vector-based PCC (MVV-PCC) for DTP-PMSM with OPF is proposed in this paper. Therein, two virtual vectors and one zero vector with optimal duty cycles are determined in each sampling period to improve the steady-state performance. A simplification strategy for selecting the optimal virtual vectors and calculating the optimal duty cycles is established to reduce the computational burden in digital implementation. Extensive comparisons are conducted to verify the effectiveness of the proposed MVV-PCC scheme., Part of ISBN 9798350396867QC 20230831

This work presents a method for multi-robot coordination based on a novel distributed nonlinear model predictive control (NMPC) formulation for trajectory optimization and its modified version to mitigate the effects of packet losses and delays in the communication among the robots. Our algorithms consider that each robot is equipped with an onboard computation unit to solve a local control problem and communicate with neighboring autonomous robots via a wireless network. The difference between the two proposed methods is in the way the robots exchange information to coordinate. The information exchange can occur in a following: 1) synchronous or 2) asynchronous fashion. By relying on the theory of the nonconvex alternating direction method of multipliers (ADMM), we show that the proposed solutions converge to a (local) solution of the centralized problem. For both algorithms, the communication exchange preserves the safety of the robots; that is, collisions with neighboring autonomous robots are prevented. The proposed approaches can be applied to various multi-robot scenarios and robot models. In this work, we assess our methods, both in simulation and with experiments, for the coordination of a team of autonomous vehicles in the following: 1) an unsupervised intersection crossing and 2) the platooning scenarios., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Learning & Autonomous Control, Transport Engineering and Logistics, Team Tamas Keviczky

In this paper, we formalise a fault-tolerant control and mitigation strategy for the energy management of renewable microgrids. The scheme is composed of a Model Predictive Control (MPC) algorithm based on a Linear Parameter Varying (LPV) model for the microgrid energy dispatch. The MPC uses fault diagnosis information in order to correctly update the LPV microgrid model, while adapting the process constraints in consonance with the level and location of faults. We show how closed-loop stability is ensured with simple quadratic terminal ingredients, provided through LMI-solvable remedies. Nonlinear simulation results of a real microgrid benchmark are included to demonstrate the effectiveness of the approach.

In this paper, we formalise a fault-tolerant control and mitigation strategy for the energy management of renewable microgrids. The scheme is composed of a Model Predictive Control (MPC) algorithm based on a Linear Parameter Varying (LPV) model for the microgrid energy dispatch. The MPC uses fault diagnosis information in order to correctly update the LPV microgrid model, while adapting the process constraints in consonance with the level and location of faults. We show how closed-loop stability is ensured with simple quadratic terminal ingredients, provided through LMI-solvable remedies. Nonlinear simulation results of a real microgrid benchmark are included to demonstrate the effectiveness of the approach.

This paper deals with the LPV control of a three-axis gimbal including fault-tolerant capabilities. First, the derivation of an analytical model for the considered system based on the robotics Serial-Link (SL) theory is derived. Then, a series of simplifications that allow obtaining a quasi-LPV model for the considered gimbal is proposed. Gain scheduling LPV controllers with PID structure are designed using pole placement by means of linear matrix inequalities (LMIs). Moreover, exploiting the sensor redundancy available in the gimbal, a virtual-sensor-based fault tolerant control (FTC) strategy is proposed. This virtual sensor uses a Recursive Least Square (RLS) estimation algorithm and an LPV observer for fault detection and estimation. Finally, the proposed LPV control scheme including the virtual sensor strategy is tested in simulation in several scenarios., Peer Reviewed, Postprint (published version)

A robust economic model predictive control approach that takes into account the reliabilityof actuators in a network ispresented for the control of a drinking water network in the presence of uncertainties in the forecasted demands required forthe predictive control design. The uncertain forecasted demand on the nominal MPC may make the optimization processintractable or, to a lesser extent, degrade the controller performance. Thus, the uncertainty on demand is taken into accountand considered unknown but bounded in a zonotopic set. Based on this uncertainty description, a robust MPC is formulatedto ensure robust constraint satisfaction, performance, stability as well as recursive feasibility throughthe formulation ofan online tube-based MPC and an accompanying appropriate terminal set. Reliability is thenmodelled based on Bayesiannetworks, such that the resulting nonlinear function accommodated in the optimization setup is presented in a pseudo-linearform by means of a linear parameter varying representation, mitigating any additional computational expense thanks to theformulation as a quadratic optimization problem. With the inclusion of a reliability index to the economic dominant cost ofthe MPC, the network users’ requirements are met whilst ensuring improved reliability, therefore decreasing short and longterm operational costs for water utility operators. Capabilities of the designed controller are demonstrated with simulatedscenarios on the Barcelona drinking water network, Peer Reviewed, Postprint (published version)

This thesis revolves around reliability and robustness of permanent-magnet synchronous drives designed for Electric Power Steering System (ePS). The first part of the thesis is devoted to reliability. In this regard, dual-star Permanent-magnet Synchronous Machines (PMSM) are adopted. Thanks to the redundancy of the phases, they are inherently more fault-tolerant than their three-phase counterparts. Moreover, unlike e.g. five- or seven-phase machine, they do not need customized converters and can be driven by a pair of off-the-shelf three-phase inverters. The machine model is discussed in the first chapter. In order to take account of the parasitic effects such as saturation and non-uniform airgap, the relationship between flux linkages and currents for different rotor positions is stored in Look-up Tables (LUT)s. The procedure for constructing the LUTs is explained. Several techniques are introduced to reduce the computational burden of the calculations by means of Finite-element (FE) analysis. Two methods are introduced for obtaining the inverse LUTs, in which the currents are outputs, and flux linkages inputs. The inverse LUTs are needed for the numerical solution of the voltage equation. The proposed method has a comparable confidence level as the FE model, however, with much less computational time. Hence, it suits system-level studies. The model is used for several studies, namely a comparative analysis of single- and dual-star machines, the impact of the loss of the inverter signals on the machine operation, and the effect ofconnecting the two star points. Then, the fault-tolerant control of the machine with one or multiple phase Open-circuit (OC) faultsis presented in the second chapter. The conventional methods based on optimization are introduced. Various control strategies which find relevance in the case of OC faults are discussed. Minimum losses, maximum torque, and full-range minimum loss control strategies are introduced. Moreover, the derating factor, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

This paper deals with the LPV control of a three-axis gimbal including fault-tolerant capabilities. First, the derivation of an analytical model for the considered system based on the robotics Serial-Link (SL) theory is derived. Then, a series of simplifications that allow obtaining a quasi-LPV model for the considered gimbal is proposed. Gain scheduling LPV controllers with PID structure are designed using pole placement by means of linear matrix inequalities (LMIs). Moreover, exploiting the sensor redundancy available in the gimbal, a virtual-sensor-based fault tolerant control (FTC) strategy is proposed. This virtual sensor uses a Recursive Least Square (RLS) estimation algorithm and an LPV observer for fault detection and estimation. Finally, the proposed LPV control scheme including the virtual sensor strategy is tested in simulation in several scenarios.

Compared with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this paper proposes an event-based fault-tolerant coordinated control (FTC) for multiple fixed-wing UAVs such that the consensus tracking of velocity and attitude is achieved in the presence of actuator faults, external disturbances and modeling uncertainties. More precisely, as opposed to static event-triggered communication mechanisms, a dynamic event-triggered communication mechanism (DECM) is devised to schedule the connected communications while avoiding the unnecessary information exchanges among UAVs, which reduces the communication burden and saves on the network resources. Meanwhile, the Zeno phenomenon is excluded in terms of guaranteeing that the period between two consecutive triggering communication is lower bounded by a positive constant. Moreover, the actuator fault, external disturbance as well as model uncertainty are treated as the lumped disturbances and estimated via the disturbance observer technique. By strict Lyapunov arguments, all closed-loop signals are proved to be uniformly ultimately bounded (UUB) and the tracking errors of velocity and attitude converge to a residual set around origin. Finally, simulation results are presented to illustrate the validity and superiority of proposed event-based control scheme., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Bart De Schutter

Compared with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this paper proposes an event-based fault-tolerant coordinated control (FTC) for multiple fixed-wing UAVs such that the consensus tracking of velocity and attitude is achieved in the presence of actuator faults, external disturbances and modeling uncertainties. More precisely, as opposed to static event-triggered communication mechanisms, a dynamic event-triggered communication mechanism (DECM) is devised to schedule the connected communications while avoiding the unnecessary information exchanges among UAVs, which reduces the communication burden and saves on the network resources. Meanwhile, the Zeno phenomenon is excluded in terms of guaranteeing that the period between two consecutive triggering communication is lower bounded by a positive constant. Moreover, the actuator fault, external disturbance as well as model uncertainty are treated as the lumped disturbances and estimated via the disturbance observer technique. By strict Lyapunov arguments, all closed-loop signals are proved to be uniformly ultimately bounded (UUB) and the tracking errors of velocity and attitude converge to a residual set around origin. Finally, simulation results are presented to illustrate the validity and superiority of proposed event-based control scheme., Accepted Author Manuscript, Team DeSchutter

[Resumen] En este artículo se plantea el problema de optimización de una ley de control para minimizar el error al conducir un AUV actuado con un único motor desde un punto de partida hasta una zona de recuperación deseada. Así mismo se muestran dos posibles soluciones de control y se discute su implementación en el vehículo. La primera utiliza algoritmos genéticos para proporcionar la acción de control óptima en cada instante, es decir, se optimiza la ley de control muestreada en función del tiempo. La segunda, por su parte, emplea los algoritmos genéticos para obtener una acción de control óptima en función de la orientación del vehículo a partir de una ley de control aproximada con una serie de Fourier, [Abstract] The present paper presents a optimization problem for a control law to minimize the error produced by driving an AUV using a single thruster from a start point to a desired recovery area. In addition, two possible control solutions are studied and their implementation in the real vehicle is discussed. In the first solution, genetic algorithms are employed to obtain the optimal control action each moment in time, i.e., a control law versus time is optimized. The second solutions also uses genetic algorithms, in this case, to obtain an optimal control action versus orientation of vehicle as of a control law based on Fourier series.

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

Purpose: This paper aims to address the spacecraft attitude regulation problem in the presence of extrinsic disturbances and actuator faults. Design/methodology/approach: Based on adaptive backstepping design technique, a new concise adaptive dual-mode control scheme is proposed, which can either use the fault information detected by fault diagnosis mechanisms or switch to the fault-unknown mode when the fault diagnosis information is non-existent for control signal generation. These two modes share an adaptive mechanism that reduces the complexity of the algorithm. Findings: The new fault-tolerant attitude control algorithm can accommodate both modes with and without fault diagnosis mechanisms. Originality/value: The proposed algorithm in this paper can be applied to both cases when the attitude control system is equipped with or without fault diagnosis capability. This also enhances the robustness of attitude control algorithm. This study performs numerical simulations and verifies that the algorithm could effectively adapt to both modes. © 2021, Emerald Publishing Limited.

This work presents a novel fault-tolerant control scheme based on active inference. Specifically, a new formulation of active inference which, unlike previous solutions, provides unbiased state estimation and simplifies the definition of probabilistically robust thresholds for fault-tolerant control of robotic systems using the free-energy. The proposed solution makes use of the sensory prediction errors in the free-energy for the generation of residuals and thresholds for fault detection and isolation of sensory faults, and it does not require additional controllers for fault recovery. Results validating the benefits in a simulated 2-DOF manipulator are presented, and future directions to improve the current fault recovery approach are discussed., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Robot Dynamics, Team Riccardo Ferrari

Quad-plane is a popular type of electric vertical and takeoff/landing (eVTOL) vehicle that hybridizes a quadrotor and a fixed-wing airplane. However, the mechanical simplicity of a quad-plane also makes it vulnerable to rotor failures. When a complete rotor fails, it becomes physically impossible to stop the quad-plane from fast yaw spinning, which further induces considerable abnormal aerodynamic forces and moments on the wing. In this paper, a novel incremental adaptive sliding mode control (I-ASMC) is proposed to address these challenges. First, by exploiting sensor measurements, it simultaneously reduces the control model dependency and the minimum possible sliding mode control/observer gains. Second, finite-time convergence is guaranteed in the Lyapunov sense. Third, the control gains are automatically adapted to their minimum possible values without prior-knowledge on the uncertainty bounds. The proposed I-ASMC method is verified on a high fidelity simulation platform with computational fluid dynamic (CFD) aerodynamic models. Simulation results demonstrate that I-ASMC can drive a quad-plane with a complete loss of a single rotor to follow a trajectory. Its robustness to aerodynamic model uncertainties and rotor faults is also better than the linear quadratic regulator (LQR) and the incremental nonlinear dynamic inversion (INDI) control. In conclusion, the reduced model dependency, implementation simplicity, and improved robustness make the proposed I-ASMC promising for enhancing quad-plane safety in real life., Aerospace Structures & Computational Mechanics

This work presents a novel fault-tolerant control scheme based on active inference. Specifically, a new formulation of active inference which, unlike previous solutions, provides unbiased state estimation and simplifies the definition of probabilistically robust thresholds for fault-tolerant control of robotic systems using the free-energy. The proposed solution makes use of the sensory prediction errors in the free-energy for the generation of residuals and thresholds for fault detection and isolation of sensory faults, and it does not require additional controllers for fault recovery. Results validating the benefits in a simulated 2-DOF manipulator are presented, and future directions to improve the current fault recovery approach are discussed., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Robot Dynamics, Team Riccardo Ferrari

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

Quad-plane is a popular type of electric vertical and takeoff/landing (eVTOL) vehicle that hybridizes a quadrotor and a fixed-wing airplane. However, the mechanical simplicity of a quad-plane also makes it vulnerable to rotor failures. When a complete rotor fails, it becomes physically impossible to stop the quad-plane from fast yaw spinning, which further induces considerable abnormal aerodynamic forces and moments on the wing. In this paper, a novel incremental adaptive sliding mode control (I-ASMC) is proposed to address these challenges. First, by exploiting sensor measurements, it simultaneously reduces the control model dependency and the minimum possible sliding mode control/observer gains. Second, finite-time convergence is guaranteed in the Lyapunov sense. Third, the control gains are automatically adapted to their minimum possible values without prior-knowledge on the uncertainty bounds. The proposed I-ASMC method is verified on a high fidelity simulation platform with computational fluid dynamic (CFD) aerodynamic models. Simulation results demonstrate that I-ASMC can drive a quad-plane with a complete loss of a single rotor to follow a trajectory. Its robustness to aerodynamic model uncertainties and rotor faults is also better than the linear quadratic regulator (LQR) and the incremental nonlinear dynamic inversion (INDI) control. In conclusion, the reduced model dependency, implementation simplicity, and improved robustness make the proposed I-ASMC promising for enhancing quad-plane safety in real life., taverne, Aerospace Structures & Computational Mechanics, Aerospace Engineering

Purpose: This paper aims to address the spacecraft attitude regulation problem in the presence of extrinsic disturbances and actuator faults. Design/methodology/approach: Based on adaptive backstepping design technique, a new concise adaptive dual-mode control scheme is proposed, which can either use the fault information detected by fault diagnosis mechanisms or switch to the fault-unknown mode when the fault diagnosis information is non-existent for control signal generation. These two modes share an adaptive mechanism that reduces the complexity of the algorithm. Findings: The new fault-tolerant attitude control algorithm can accommodate both modes with and without fault diagnosis mechanisms. Originality/value: The proposed algorithm in this paper can be applied to both cases when the attitude control system is equipped with or without fault diagnosis capability. This also enhances the robustness of attitude control algorithm. This study performs numerical simulations and verifies that the algorithm could effectively adapt to both modes. © 2021, Emerald Publishing Limited.

We investigate robust fault-tolerant control pertaining to Takagi–Sugeno (TS) fuzzy nonlinear systems with bounded disturbances, actuator failures, and time delays. A new fault model based on a sampled-data scheme that is able to satisfy certain criteria in relation to actuator fault matrix is introduced. Specifically, we formulate a reliable controller with state feedback, such that the resulting closed-loop-fuzzy system is robust, asymptotically stable, and able to satisfy a prescribed H∞ performance constraint. Linear matrix inequality (LMI) together with a proper construction of the Lyapunov–Krasovskii functional is leveraged to derive delay-dependent sufficient conditions with respect to the existence of robust H∞ controller. It is straightforward to obtain the solution by using the MATLAB LMI toolbox. We demonstrate the effectiveness of the control law and less conservativeness of the results through two numerical simulations.

Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant Control (FTC) model in 2 MW three-bladed wind turbines implemented using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures, and Turbulence (FAST) wind turbine simulator. This control strategy is potentially supported by the health status of the gearbox, which was predicted by means of algorithms and quantified in an indicator denominated as a merge developed by SMARTIVE, a pioneering of in this idea. Fuzzy logic was employed in order to decide whether to down-regulate the output power or not, and to which level to adjust to the needs of the turbines. Simulation results demonstrated that a reduction in the power output resulted in a safer operation, since the stresses withstood by the blades and tower significantly decreased. Moreover, the results supported empirically that a diminution in the generator torque and speed was acheived, resulting in a drop in the gearbox bearing and oil temperatures. By implementing this power-derating FTC, the downtime due to failure stops could be controlled, and thus the power production noticeably grew. It has been estimated that more than 325,000 tons of CO2 could be avoided yearly if implemented globally., This work has been partially funded by the Spanish Centro para el Desarollo Tecnológico Industrial, grant number CDTI-IDI 20191294; the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), grant number 2017 SGR 1348; and Ministerio de Ciencia e Innovación, grant number PID2020-120314RB-I00., Peer Reviewed, Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant, Postprint (published version)

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized.

Purpose: This paper aims to address the spacecraft attitude regulation problem in the presence of extrinsic disturbances and actuator faults. Design/methodology/approach: Based on adaptive backstepping design technique, a new concise adaptive dual-mode control scheme is proposed, which can either use the fault information detected by fault diagnosis mechanisms or switch to the fault-unknown mode when the fault diagnosis information is non-existent for control signal generation. These two modes share an adaptive mechanism that reduces the complexity of the algorithm. Findings: The new fault-tolerant attitude control algorithm can accommodate both modes with and without fault diagnosis mechanisms. Originality/value: The proposed algorithm in this paper can be applied to both cases when the attitude control system is equipped with or without fault diagnosis capability. This also enhances the robustness of attitude control algorithm. This study performs numerical simulations and verifies that the algorithm could effectively adapt to both modes. © 2021, Emerald Publishing Limited.

A major goal in modern flight control systems is the need for improving reliability. This work presents a health-aware and fault-tolerant control approach for an octorotor UAV that allows distributing the control effort among the available actuators based on their health information. However, it is worth mentioning that, in the case of actuator fault occurrence, a reliability improvement can come into conflict with UAV controllability. Therefore, system reliability sensitivity is redefined and modified to prevent uncontrollable situations during the UAV’s mission. The priority given to each actuator is related to its importance in system reliability. Moreover, the proposed approach can reconfigure the controller to compensate actuator faults and improve the overall system reliability or delay maintenance tasks., Peer Reviewed, Postprint (published version)

We present a fault tolerant control scheme for robot manipulators based on active inference. The proposed solution makes use of the sensory prediction errors in the free-energy to simplify the residuals and thresholds generation for fault detection and isolation and does not require additional controllers for fault recovery. Results validating the benefits in a simulated 2DOF manipulator are presented and the limitations of the current approach are highlighted., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Robot Dynamics, Team Jan-Willem van Wingerden

We present a fault tolerant control scheme for robot manipulators based on active inference. The proposed solution makes use of the sensory prediction errors in the free-energy to simplify the residuals and thresholds generation for fault detection and isolation and does not require additional controllers for fault recovery. Results validating the benefits in a simulated 2DOF manipulator are presented and the limitations of the current approach are highlighted., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Robot Dynamics, Team Jan-Willem van Wingerden

This article proposes a fault-tolerant control (FTC) strategy based on virtual actuator and sensor for discrete-time descriptor systems subject to actuator and sensor faults. The fault-tolerant closed-loop system, which includes the nominal controller and observer, as well as the virtual actuator and the virtual sensor, hides the effects of faults. When an observer-based state-feedback law is considered, the existence of algebraic loop may prevent the practical implementation due to the current algebraic states depending on the current control input, that affects also the implementation of the virtual actuator/sensor. To deal with this issue, an observer-based delayed feedback controller and a delayed virtual actuator are proposed for discrete-time descriptor systems. Furthermore, the satisfaction of the separation principle is shown, and an improved admissibility condition is developed for the design of the controller and virtual actuator/sensor. Finally, some simulation results including an electrical circuit are used to demonstrate the applicability of the proposed methods.

This paper proposes incremental nonsingular terminal sliding mode control for a class of multi-input and multi-output nonlinear systems considering model uncertainties, external disturbances, and sudden actuator faults. This method is free from singularity because it does not involve any negative fractional power. The convergence time in both reaching and sliding phases are proved to be finite. Moreover, by fully exploiting sensor measurements, the proposed incremental control method simultaneously reduces model dependency and the uncertainty remaining in the closed-loop system. The reduction of model dependency simplifies the implementation process and reduces the computational load, while the reduction of uncertainty decreases the minimum possible sliding mode control gains, which is beneficial to chattering reduction. These merits are verified by a quadrotor trajectory tracking problem. Simulation results demonstrate that the proposed method has better robustness against model uncertainties, gusts, and actuator faults than the model-based nonsingular terminal sliding mode control in the literature., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Control & Simulation

This paper proposes an Incremental Sliding Mode Control driven by Sliding Mode Disturbance Observers (INDI-SMC/SMDO), with application to a quadrotor fault tolerant control problem. By designing the SMC/SMDO based on the control structure of the sensor-based Incremental Nonlinear Dynamic Inversion (INDI), instead of the model-based Nonlinear Dynamic Inversion (NDI) in the literature, the model dependency of the controller and the uncertainties in the closed-loop system are simultaneously reduced. This allows INDI-SMC/SMDO to passively resist a wider variety of faults and external disturbances using continuous control inputs with lower control and observer gains. When applied to a quadrotor, both numerical simulations and real-world flight tests demonstrate that INDI based SMC/SMDO has better performance and robustness over NDI based SMC/SMDO, in the presence of model uncertainties, wind disturbances, and sudden actuator faults. Moreover, the implementation process is simplified because of the reduced model dependency and smaller uncertainty variations of INDI-SMC/SMDO. Therefore, the proposed control method can be easily implemented to improve the performance and survivability of quadrotors in real life., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Control & Simulation

The swift growth of air traffic volume stresses the importance of flight safety enhancement. Statistical data shows that fly-by-wire technology with automatic flight control systems can effectively reduce the fatal accident rate of loss of control in-flight. Although the dynamics of an aircraft are nonlinear and time-varying, it is common practice to design flight control laws based on local linear time-invariant (LTI) dynamic models, and apply gain-scheduling method. Here, the flight envelope is divided into many smaller operating regimes, and LTI model-based controllers are designed and tuned for each of them. However, this approach is cumbersome and cannot guarantee flight stability and performance in-between operational points. In view of the challenges encountered by LTI model-based control, nonlinear control methods have attracted attention from the flight control community. Nonlinear dynamic inversion (NDI) and backstepping (BS) are two frequently used nonlinear control methods in flight control. These two approaches cancel the nonlinearities in the closed loop using a nonlinear model of the system. However, mismatches between the model and real dynamics inevitability exist, especially when an aircraft encounters atmospheric disturbances and when sudden actuator faults or even structural damages occur. To enhance the robustness of model-based nonlinear control methods to model mismatches, a commonly adopted approach is to augment them with online model identification. This process, however, is computational intensive and requires sufficient excitation, which can make an impaired aircraft fly out of the diminished safe flight envelope. In consideration of these challenges, the main goal of this thesis is: To design a stability-guaranteed nonlinear flight control framework with reduced model dependency and enhanced robustness., Control & Simulation

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme

This paper proposes incremental nonsingular terminal sliding mode control for a class of multi-input and multi-output nonlinear systems considering model uncertainties, external disturbances, and sudden actuator faults. This method is free from singularity because it does not involve any negative fractional power. The convergence time in both reaching and sliding phases are proved to be finite. Moreover, by fully exploiting sensor measurements, the proposed incremental control method simultaneously reduces model dependency and the uncertainty remaining in the closed-loop system. The reduction of model dependency simplifies the implementation process and reduces the computational load, while the reduction of uncertainty decreases the minimum possible sliding mode control gains, which is beneficial to chattering reduction. These merits are verified by a quadrotor trajectory tracking problem. Simulation results demonstrate that the proposed method has better robustness against model uncertainties, gusts, and actuator faults than the model-based nonsingular terminal sliding mode control in the literature., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Control & Simulation

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with an unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation, and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation, or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust, adaptive, and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

Hydraulic actuators are used extensively in various equipment including aircraft, robots and heavy-duty machinery. One of the common problems in hydraulic systems is occurrence of faults such as the actuator internal leakage. Some faults need to be detected properly to prevent actuator malfunctioning. For example, the internal leakage deteriorates the system performance as it causes the fluid to move between cylinder chambers. Model-based fault detection techniques require a model of the hydraulic actuator and faults or an estimation of system parameters. In practice, there is no accurate model to describe some faults. Application of data-driven fault detection techniques is more desirable. One of the main challenges, due to the faults, in hydraulic actuation systems is the controller design. If a controller is not able to compensate for fault effects, it may lead to considerable losses. Therefore, there is a need for designing fault-tolerant controllers (FTCs). Data-driven techniques of controller design are good solutions to overcome challenges associated with obtaining models or estimating system and fault parameters. In this thesis, the applications of data-driven techniques of the fault detection and controller design to the hydraulic actuators are investigated and exemplified with detection of the internal leakage fault and design of a control system tolerant to the leakage. First, a set of multiscale measures is quantified in various healthy and faulty operating modes and a comparison is carried out, between the measures, to identify the most reliable indicator(s) in detecting various levels of the leakage. Comparison results indicate that the wavelet transform is the best technique, amongst the employed multiscale measures, for the internal leakage detection. Next, a fractional-order PID (FOPID) position controller is designed using experimental data. Efficacy of the designed controller in tracking various reference inputs, in presence of different system un

The swift growth of air traffic volume stresses the importance of flight safety enhancement. Statistical data shows that fly-by-wire technology with automatic flight control systems can effectively reduce the fatal accident rate of loss of control in-flight. Although the dynamics of an aircraft are nonlinear and time-varying, it is common practice to design flight control laws based on local linear time-invariant (LTI) dynamic models, and apply gain-scheduling method. Here, the flight envelope is divided into many smaller operating regimes, and LTI model-based controllers are designed and tuned for each of them. However, this approach is cumbersome and cannot guarantee flight stability and performance in-between operational points. In view of the challenges encountered by LTI model-based control, nonlinear control methods have attracted attention from the flight control community. Nonlinear dynamic inversion (NDI) and backstepping (BS) are two frequently used nonlinear control methods in flight control. These two approaches cancel the nonlinearities in the closed loop using a nonlinear model of the system. However, mismatches between the model and real dynamics inevitability exist, especially when an aircraft encounters atmospheric disturbances and when sudden actuator faults or even structural damages occur. To enhance the robustness of model-based nonlinear control methods to model mismatches, a commonly adopted approach is to augment them with online model identification. This process, however, is computational intensive and requires sufficient excitation, which can make an impaired aircraft fly out of the diminished safe flight envelope. In consideration of these challenges, the main goal of this thesis is: To design a stability-guaranteed nonlinear flight control framework with reduced model dependency and enhanced robustness., Control & Simulation

This paper proposes an Incremental Sliding Mode Control driven by Sliding Mode Disturbance Observers (INDI-SMC/SMDO), with application to a quadrotor fault tolerant control problem. By designing the SMC/SMDO based on the control structure of the sensor-based Incremental Nonlinear Dynamic Inversion (INDI), instead of the model-based Nonlinear Dynamic Inversion (NDI) in the literature, the model dependency of the controller and the uncertainties in the closed-loop system are simultaneously reduced. This allows INDI-SMC/SMDO to passively resist a wider variety of faults and external disturbances using continuous control inputs with lower control and observer gains. When applied to a quadrotor, both numerical simulations and real-world flight tests demonstrate that INDI based SMC/SMDO has better performance and robustness over NDI based SMC/SMDO, in the presence of model uncertainties, wind disturbances, and sudden actuator faults. Moreover, the implementation process is simplified because of the reduced model dependency and smaller uncertainty variations of INDI-SMC/SMDO. Therefore, the proposed control method can be easily implemented to improve the performance and survivability of quadrotors in real life., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Control & Simulation

This paper presents two Fault Tolerant schemes for more reliable spacing control of an autonomous vehicle. The nonlinear longitudinal model of the vehicle is described by the Lipschitz representation. Based upon this representation, a state feedback integral controller is designed, as well as, fault estimation observers. This proposal work is focused on Lyapunov theory ensuring 2-gain norm in the development of the Linear Matrix Inequality constraints. The design also includes an estimation of vehicle states and an additive fault with the purpose of achieving highly robust and safe vehicle inter-distance control. To carry out this work, experiments are highlighted on prototype vehicle to validate the proposed FTC scheme in different autonomous driving scenarios.

In this paper, the control problem of a service robot under exogenous forces that might be exerted on the system is addressed, considering them as faults. Proposed approach is based on using a fault estimation scheme together with a Robust Unknown-Input Observer (RUIO) that allows to estimate the fault as well as the robot state, along with a state feedback control strategy. After the fault occurrence, from the fault estimation, a feedforward control action is added to the feedback control action to compensate the fault effect. To cope with the robot non-linearity, its model is transformed into a quasi-LPV model. Both the state-feedback and RUIO are designed using an LMI-based approach considering a gain-scheduling alike scheme, where global conditions have been considered. To illustrate the proposed fault-tolerant approach, a mobile service robot TIAGo, developed by PAL robotics, is used.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., In this paper, the control problem of a service robot under exogenous forces that might be exerted on the system is addressed, considering them as faults. Proposed approach is based on using a fault estimation scheme together with a Robust Unknown-Input Observer (RUIO) that allows to estimate the fault as well as the robot state, along with a state feedback control strategy. After the fault occurrence, from the fault estimation, a feedforward control action is added to the feedback control action to compensate the fault effect. To cope with the robot non-linearity, its model is transformed into a quasi-LPV model. Both the state-feedback and RUIO are designed using an LMI-based approach considering a gain-scheduling alike scheme, where global conditions have been considered. To illustrate the proposed fault-tolerant approach, a mobile service robot TIAGo, developed by PAL robotics, is used., Peer Reviewed, Postprint (author's final draft)

This paper focuses on the longitudinal control of an Airbus passenger aircraft in the presence of elevator jamming faults. In particular, in this paper, we address permanent and temporary actuator jamming faults using a novel reconfigurable fault-tolerant predictive control design. Due to their different consequences on the available control authority and fault duration, the above 2 actuator jamming faults need to be distinguished so that appropriate control reconfigurations can be adopted accordingly. Their similarity in symptoms, however, prevents an effective discrimination of the root cause of the jamming when using only a passive fault-diagnosis approach. Hence, we propose the use of model predictive control (MPC) as a fault-tolerant controller to actively help the fault-detection (FD) unit discriminate between a permanent and a temporary jamming fault, while ensuring the performance of the aircraft. The MPC controller and the FD unit closely interact during the detection and diagnosis phases. In particular, every time a fault is detected, the FD module commands the MPC controller to perform a predefined sequence of reconfigurations to diagnose the root cause of the fault. An artificial reference signal that accounts for changes in the actuator operative ranges is used to guide the system through this sequence of reconfigurations. Our strategy is demonstrated on an Airbus passenger aircraft simulator., Networked Cyber-Physical Systems

Proportional integral derivative (PID) regulators are the most practical control schemes for industrial wind turbines. The key to PID design is the determination of the control parameter gains, which motivated our attempts to construct an adaptive PID control for wind turbines allowing auto-tuning of the gains without the need for trial and error processes. By equipping a novel PID-based fault-tolerant controller with a Nussbaum-type function, a robust adaptive and fault-tolerant control scheme is developed for wind turbines. Compared with available methods, the proposed controller has advantages, such as the ability for dealing with complete nonlinear dynamics of wind turbines, including model uncertainty, ability to ensure system stability by using an adaptive self-tuning gain algorithm, and robustness against wind speed variation. Furthermore, it has the ability to accommodate unexpected actuator faults and the accommodation of an unknown control direction. However, the salient feature of the proposed controller lies in its simple structure and inexpensive online computational demands while dealing with the nonlinear dynamics of wind turbines and unknown disturbances. It is shown that the proposed pitch angle controller remains continuous and smooth and all the closed-loop system signals are guaranteed to be uniformly ultimately bounded. Theoretical analysis and numerical simulations are presented to confirm the effectiveness of the proposed control strategy.

A data‐driven methodology that includes the unfalsified control concept in the framework of fault diagnosis and isolation (FDI) and fault‐tolerant control (FTC) is presented. The selection of the appropriate controller from a bank of controllers in a switching supervisory control setting is performed by using an adequate FDI outcome. By combining simultaneous online performance assessment of multiple controllers with the fault diagnosis decision from structured hypothesis tests, a diagnosis statement regarding what controller is most suitable to deal with the current (nominal or faulty) mode of the plant is obtained. Switching strategies that use the diagnosis statement are also proposed. This approach is applied to a nonlinear experimentally validated model of the breathing system of a polymer electrolyte membrane fuel cell. The results show the effectiveness of this FDI–fault‐tolerant control data‐driven methodology.

In this paper, we address the problem of distributed reconfiguration of networked control systems upon the removal of misbehaving sensors and actuators. In particular, we consider systems with redundant sensors and actuators cooperating to recover from faults. Reconfiguration is performed while minimizing a steady-state estimation error covariance and quadratic control cost. A model-matching condition is imposed on the reconfiguration scheme. It is shown that the reconfiguration and its underlying computation can be distributed. Using an average dwell-time approach, the stability of the distributed reconfiguration scheme under finite-time termination is analyzed. The approach is illustrated in a numerical example., QC 20190117, CERCES

Aplicat embargament des de la data de defensa fins a l'1 d'octubre de 2020, Premi extraordinari doctorat UPC curs 2017-2018. Àmbit d’Enginyeria Industrial. Premio IDOM a la mejor Tesis Doctoral en Control Inteligente otorgado por el Comité Español d’Automática, This dissertation intends to provide theoretical and practical contributions on estimation, diagnosis and control of complex systems, especially in the mathematical form of descriptor systems. The research is motivated by real applications, such as water networks and power systems, which require a control system to provide a proper management able to take into account their specific features and operating limits in presence of uncertainties related to their operation and failures from component malfunctions. Such a control system is expected to provide an optimal operation to obtain efficient and reliable performance. State estimation is an essential tool, which can be used not only for fault diagnosis but also for the controller design. To achieve a satisfactory robust performance, set theory is chosen to build a general framework for descriptor systems subject to uncertainties. Under certain assumptions, these uncertainties are propagated and bounded by deterministic sets that can be explicitly characterized at each iteration step. Moreover, set-invariance characterizations for descriptor systems are also of interest to describe the steady performance, which can also be used for active mode detection. For the controller design for complex systems, new developments of economic model predictive control (EMPC) are studied taking into account the case of underlying periodic behaviors. The EMPC controller is designed to be recursively feasible even with sudden changes in the economic cost function and the closed-loop convergence is guaranteed. Besides, a robust technique is plugged into the EMPC controller design to maintain these closed-loop properties in presence of uncertainties. Engineering applications modeled as descriptor systems are presented to illustrate these control strategies. From the real applications, some additional difficulties are solved, such as using a two-layer control strategy to avoid binary variables in real-time optimizations and using nonline, Esta tesis propone contribuciones de carácter teórico y aplicado para la estimación del estado, el diagnóstico y el control óptimo de sistemas dinámicos complejos en particular, para los sistemas descriptores, incluyendo la capacidad de tolerancia a fallos. La motivación de la tesis proviene de aplicaciones reales, como redes de agua y sistemas de energía, cuya naturaleza crítica requiere necesariamente un sistema de control para una gestión capaz de tener en cuenta sus características específicas y límites operativos en presencia de incertidumbres relacionadas con su funcionamiento, así como fallos de funcionamiento de los componentes. El objetivo es conseguir controladores que mejoren tanto la eficiencia como la fiabilidad de dichos sistemas. La estimación del estado es una herramienta esencial que puede usarse no solo para el diagnóstico de fallos sino también para el diseño del control. Con este fin, se ha decidido utilizar metodologías intervalares, o basadas en conjuntos, para construir un marco general para los sistemas de descriptores sujetos a incertidumbres desconocidas pero acotadas. Estas incertidumbres se propagan y delimitan mediante conjuntos que se pueden caracterizar explícitamente en cada instante. Por otra parte, también se proponen caracterizaciones basadas en conjuntos invariantes para sistemas de descriptores que permiten describir comportamientos estacionarios y resultan útiles para la detección de modos activos. Se estudian también nuevos desarrollos del control predictivo económico basado en modelos (EMPC) para tener en cuenta posibles comportamientos periódicos en la variación de parámetros o en las perturbaciones que afectan a estos sistemas. Además, se demuestra que el control EMPC propuesto garantiza la factibilidad recursiva, incluso frente a cambios repentinos en la función de coste económico y se garantiza la convergencia en lazo cerrado. Por otra parte, se utilizan técnicas de control robusto pata garantizar que las estrategias de co, Award-winning, Postprint (published version)