Ponències/Comunicacions de congressos
http://hdl.handle.net/2117/1126
2024-03-29T02:03:13Z
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Neuro-fuzzy Takagi Sugeno observer for fault diagnosis in wind turbines
http://hdl.handle.net/2117/405005
Neuro-fuzzy Takagi Sugeno observer for fault diagnosis in wind turbines
Pérez Pérez, Esvan de Jesús; Puig Cayuela, Vicenç; López Estrada, Francisco Ronay; Valencia Palomo, Guillermo; Santos Ruiz, Ildeberto
This work proposes a method for fault diagnosis based on Takagi Sugeno (TS) observers and convex models identified with a multioutput adaptive neuro-fuzzy inference system (MANFIS) derived from structural analysis. A bank of zonotopic TS observers is implemented to detect sensors and actuators faults. Unlike other works that require data from fault scenarios to train the MANFIS neural network, only fault-free data are considered. In addition, uncertainty related to aerodynamic loads and measurement noise is considered for testing the proposed method's robustness. The method performance is evaluated using measurements from a 5 MW wind turbine benchmark.
2024-03-20T10:32:42Z
Pérez Pérez, Esvan de Jesús
Puig Cayuela, Vicenç
López Estrada, Francisco Ronay
Valencia Palomo, Guillermo
Santos Ruiz, Ildeberto
This work proposes a method for fault diagnosis based on Takagi Sugeno (TS) observers and convex models identified with a multioutput adaptive neuro-fuzzy inference system (MANFIS) derived from structural analysis. A bank of zonotopic TS observers is implemented to detect sensors and actuators faults. Unlike other works that require data from fault scenarios to train the MANFIS neural network, only fault-free data are considered. In addition, uncertainty related to aerodynamic loads and measurement noise is considered for testing the proposed method's robustness. The method performance is evaluated using measurements from a 5 MW wind turbine benchmark.
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Integral sliding-mode fault-tolerant pitch control of wind turbines
http://hdl.handle.net/2117/404934
Integral sliding-mode fault-tolerant pitch control of wind turbines
Serrano, Fernando E.; Puig Cayuela, Vicenç
In this paper, an integral sliding-mode fault-tolerant pitch control of wind turbines is presented. The proposed approach uses a fault diagnosis strategy which consists of a sliding-mode fault diagnosis observer. This observer is based on using an integral sliding-mode estimation scheme by using a suitable Lyapunov functional. Based on the previous fault diagnosis strategy, an integral sliding mode controller is designed by selecting an appropriate sliding mode surface in order to obtain the fault tolerant-control law obtained by also selecting appropriated Lyapunov functional. A wind-turbine case study is used to validate in simulation the the proposed approach.
2024-03-19T12:44:02Z
Serrano, Fernando E.
Puig Cayuela, Vicenç
In this paper, an integral sliding-mode fault-tolerant pitch control of wind turbines is presented. The proposed approach uses a fault diagnosis strategy which consists of a sliding-mode fault diagnosis observer. This observer is based on using an integral sliding-mode estimation scheme by using a suitable Lyapunov functional. Based on the previous fault diagnosis strategy, an integral sliding mode controller is designed by selecting an appropriate sliding mode surface in order to obtain the fault tolerant-control law obtained by also selecting appropriated Lyapunov functional. A wind-turbine case study is used to validate in simulation the the proposed approach.
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Job shop scheduling with limited-capacity buffers using constraint programming and genetic algorithms
http://hdl.handle.net/2117/404930
Job shop scheduling with limited-capacity buffers using constraint programming and genetic algorithms
Pedrosa Alias, Javier; Puig Cayuela, Vicenç
This article aims to propose a new approach for solving production planning and scheduling in the process industries, in such a way to be adaptable to any manufacturing plant and exploring the use of innovative AI-style technologies. The main contributions of the work are: (i) the design of a specific data format to describe any manufacturing plant (including resources, layout and production recipes), being the input of the method; and (ii) the consideration of limited-capacity production lines with intermediate and final buffers in the optimization. The method involves two stages: the first one corresponds to a deterministic optimization algorithm based on Constraint Programming modelling to solve the JSSP in an ideal scenario with no storage limitation; while the second one is a Genetic Algorithm that only comes into play when the solutions obtained from the first one are infeasible for the available storage, so it is a complementary layer to try to solve the mismatches stochastically.
2024-03-19T12:19:52Z
Pedrosa Alias, Javier
Puig Cayuela, Vicenç
This article aims to propose a new approach for solving production planning and scheduling in the process industries, in such a way to be adaptable to any manufacturing plant and exploring the use of innovative AI-style technologies. The main contributions of the work are: (i) the design of a specific data format to describe any manufacturing plant (including resources, layout and production recipes), being the input of the method; and (ii) the consideration of limited-capacity production lines with intermediate and final buffers in the optimization. The method involves two stages: the first one corresponds to a deterministic optimization algorithm based on Constraint Programming modelling to solve the JSSP in an ideal scenario with no storage limitation; while the second one is a Genetic Algorithm that only comes into play when the solutions obtained from the first one are infeasible for the available storage, so it is a complementary layer to try to solve the mismatches stochastically.
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Robust tube-based TS-MPC for safe coordination of autonomous vehicle
http://hdl.handle.net/2117/404923
Robust tube-based TS-MPC for safe coordination of autonomous vehicle
Requena Gallego, José; Puig Cayuela, Vicenç
In this work, a robust vehicle control scheme is proposed, which is capable of coordinating with nearby vehicles in order to optimally compute control actions that achieve collision-free overtaking maneuvers. The control actions are computed online by a global model predictive control (MPC) controller, which assumes a nominal disturbance-free vehicle model. To reduce the computational burden of the MPCs optimization problem, the vehicle model is reformulated into a pseudo-linear Takagi-Sugeno (TS) representation. Furthermore, the mismatch error between the real and the nominal model is corrected by a local TS H8-optimal state-feedback controller. Moreover, the robust feasibility of the MPCs optimization problem is guaranteed by implementing a tube-based architecture. Finally, the proposed control scheme is tested and validated in a high-fidelity simulation, in which the controlled vehicle was capable of overtaking multiple vehicles while rejecting disturbances.
2024-03-19T11:59:30Z
Requena Gallego, José
Puig Cayuela, Vicenç
In this work, a robust vehicle control scheme is proposed, which is capable of coordinating with nearby vehicles in order to optimally compute control actions that achieve collision-free overtaking maneuvers. The control actions are computed online by a global model predictive control (MPC) controller, which assumes a nominal disturbance-free vehicle model. To reduce the computational burden of the MPCs optimization problem, the vehicle model is reformulated into a pseudo-linear Takagi-Sugeno (TS) representation. Furthermore, the mismatch error between the real and the nominal model is corrected by a local TS H8-optimal state-feedback controller. Moreover, the robust feasibility of the MPCs optimization problem is guaranteed by implementing a tube-based architecture. Finally, the proposed control scheme is tested and validated in a high-fidelity simulation, in which the controlled vehicle was capable of overtaking multiple vehicles while rejecting disturbances.
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Zonotopic set-membership state estimation for switched LPV systems
http://hdl.handle.net/2117/404922
Zonotopic set-membership state estimation for switched LPV systems
Zhang, Shuang; Puig Cayuela, Vicenç; Ifqir, Sara
This paper addresses the state estimation problem for switched discrete-time Linear Parameter Varying (LPV) systems with mensurable and unmeasurable scheduling parameters. A zonotopic switched polytopic state estimator, considering parameter uncertainty, is proposed based on a Set-Membership Approach (SMA). Taking Average Dwell Time (ADT) into account, a new criterion is proposed to guarantee the convergence of the estimation. An application to vehicle lateral dynamics state estimation is used as case study. Simulation results reveal the effectiveness of the proposed algorithm and demonstrate advantages over the existing methods.
2024-03-19T11:50:20Z
Zhang, Shuang
Puig Cayuela, Vicenç
Ifqir, Sara
This paper addresses the state estimation problem for switched discrete-time Linear Parameter Varying (LPV) systems with mensurable and unmeasurable scheduling parameters. A zonotopic switched polytopic state estimator, considering parameter uncertainty, is proposed based on a Set-Membership Approach (SMA). Taking Average Dwell Time (ADT) into account, a new criterion is proposed to guarantee the convergence of the estimation. An application to vehicle lateral dynamics state estimation is used as case study. Simulation results reveal the effectiveness of the proposed algorithm and demonstrate advantages over the existing methods.
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A model predictive scheduling strategy for coordinated inland vessel navigation and bridge operation
http://hdl.handle.net/2117/404421
A model predictive scheduling strategy for coordinated inland vessel navigation and bridge operation
Segovia Castillo, Pablo; Puig Cayuela, Vicenç; Reppa, Vasso
This paper presents the design of a model predictive scheduling strategy to address the inland waterborne transport (IWT) problem considering bridges that must open to enable vessel passage. The main contribution is the formulation of a control-oriented model of the problem, including propositional logic expressions that characterize system behavior and their conversion into (in)equality constraints. The resulting model is embedded into a predictive scheduling approach to determine bridge opening timetables and vessel passage times in a coordinated manner. The effectiveness of the strategy is demonstrated on a realistic case study based on the Rhine-Alpine corridor.
© 2023 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.
2024-03-13T12:13:30Z
Segovia Castillo, Pablo
Puig Cayuela, Vicenç
Reppa, Vasso
This paper presents the design of a model predictive scheduling strategy to address the inland waterborne transport (IWT) problem considering bridges that must open to enable vessel passage. The main contribution is the formulation of a control-oriented model of the problem, including propositional logic expressions that characterize system behavior and their conversion into (in)equality constraints. The resulting model is embedded into a predictive scheduling approach to determine bridge opening timetables and vessel passage times in a coordinated manner. The effectiveness of the strategy is demonstrated on a realistic case study based on the Rhine-Alpine corridor.
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An output-feedback fault-tolerant control approach for multiple faults
http://hdl.handle.net/2117/404420
An output-feedback fault-tolerant control approach for multiple faults
Pazera, Marcin; Witczak, Marcin; Puig Cayuela, Vicenç; Aubrun, C
This paper proposes an output-feedback fault-tolerant control approach for multiple faults. The proposed approach is able to deal with both sensors and actuator faults. Moreover, the disturbances are assumed to be bounded within an ellipsoidal sets. The proposed strategy boils down to solving a set of LMIs along with an auxiliary parameter, which determines the convergence rate of the approach. Finally, the proposed strategy is illustrated with two-rotor aerodynamical system.
© 2023 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
2024-03-13T12:10:54Z
Pazera, Marcin
Witczak, Marcin
Puig Cayuela, Vicenç
Aubrun, C
This paper proposes an output-feedback fault-tolerant control approach for multiple faults. The proposed approach is able to deal with both sensors and actuator faults. Moreover, the disturbances are assumed to be bounded within an ellipsoidal sets. The proposed strategy boils down to solving a set of LMIs along with an auxiliary parameter, which determines the convergence rate of the approach. Finally, the proposed strategy is illustrated with two-rotor aerodynamical system.
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Gaussian sampling approach to deal with imbalanced telemetry datasets in industrial applications
http://hdl.handle.net/2117/404415
Gaussian sampling approach to deal with imbalanced telemetry datasets in industrial applications
Galve Ceamanos, Sergio; Puig Cayuela, Vicenç; Vilajosana Guillén, Xavier
Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach.
© 2023 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
2024-03-13T11:51:56Z
Galve Ceamanos, Sergio
Puig Cayuela, Vicenç
Vilajosana Guillén, Xavier
Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach.
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Assessing a statistical and a set-based approach for remaining useful life prediction
http://hdl.handle.net/2117/404387
Assessing a statistical and a set-based approach for remaining useful life prediction
Khoury, Boutrous; Thuillier, Julien; Jha, Mayank Shekhar; Puig Cayuela, Vicenç; Theilliol, Didier
In this paper, an assessment of two methods of uncertainty quantification in prognostics is undertaken. The two methods, the Inverse First Order Reliability Method (IFORM) and set-based reachability analysis for prognostics are considered. The IFORM approach permits the generation of confidence bounds that allows for the calculation of RUL values corresponding to the specified user-defined probability levels. On the other hand, uncertainty quantification can be achieved by means of set-based reachability analysis. A Zono-topic Kalman filter (ZKF) is proposed to take into account a damage-model such that at each propagation time, with the estimated state (degradation) and its uncertainty, a propagation of zonotopic sets can be produced. Coming from two different schools of thought, the statistical and set-based theory, both schemes are explored and tested on a case study in simulation.
© 2023 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
2024-03-13T10:28:38Z
Khoury, Boutrous
Thuillier, Julien
Jha, Mayank Shekhar
Puig Cayuela, Vicenç
Theilliol, Didier
In this paper, an assessment of two methods of uncertainty quantification in prognostics is undertaken. The two methods, the Inverse First Order Reliability Method (IFORM) and set-based reachability analysis for prognostics are considered. The IFORM approach permits the generation of confidence bounds that allows for the calculation of RUL values corresponding to the specified user-defined probability levels. On the other hand, uncertainty quantification can be achieved by means of set-based reachability analysis. A Zono-topic Kalman filter (ZKF) is proposed to take into account a damage-model such that at each propagation time, with the estimated state (degradation) and its uncertainty, a propagation of zonotopic sets can be produced. Coming from two different schools of thought, the statistical and set-based theory, both schemes are explored and tested on a case study in simulation.
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Robust data-driven TS MPC-based reference governor for an autonomous racing vehicle considering battery state of charge
http://hdl.handle.net/2117/403736
Robust data-driven TS MPC-based reference governor for an autonomous racing vehicle considering battery state of charge
Samada Rigo, Sergio Emil; Puig Cayuela, Vicenç; Nejjari Akhi-Elarab, Fatiha
A reference governor approach based on model predictive control (MPC-RG) for an autonomous racing vehicle is developed in this work. This control strategy avoids constraint violations and includes online health management capabilities by solving a multi-objective optimization problem. In this case, a trade-off between the maximization of the state of charge of the battery and the longitudinal velocity, even the minimization of the control actions variation is carried out. In turn, the invariant zonotopic sets analysis ensures the convergence of states to a stable region. On the other hand, the proposed control scheme also combines a robust states feedback linear quadratic regulator (LQR) with a Kalman filter (KF) estimator to compensate for model uncertainty and exogenous disturbances, as well as, to estimate the unmeasured lateral velocity. Moreover, to represent the non-linear behaviour of the vehicle, a data-driven neuro-fuzzy Takagi-Sugeno (TS) model is employed. The developed approach is tested and evaluated in realistic environments by means of a simulated 1/10 Scale RC car.
© 2023 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.
2024-03-05T13:55:52Z
Samada Rigo, Sergio Emil
Puig Cayuela, Vicenç
Nejjari Akhi-Elarab, Fatiha
A reference governor approach based on model predictive control (MPC-RG) for an autonomous racing vehicle is developed in this work. This control strategy avoids constraint violations and includes online health management capabilities by solving a multi-objective optimization problem. In this case, a trade-off between the maximization of the state of charge of the battery and the longitudinal velocity, even the minimization of the control actions variation is carried out. In turn, the invariant zonotopic sets analysis ensures the convergence of states to a stable region. On the other hand, the proposed control scheme also combines a robust states feedback linear quadratic regulator (LQR) with a Kalman filter (KF) estimator to compensate for model uncertainty and exogenous disturbances, as well as, to estimate the unmeasured lateral velocity. Moreover, to represent the non-linear behaviour of the vehicle, a data-driven neuro-fuzzy Takagi-Sugeno (TS) model is employed. The developed approach is tested and evaluated in realistic environments by means of a simulated 1/10 Scale RC car.