Articles de revista
http://hdl.handle.net/2117/1125
20171214T06:06:19Z

System reliability aware model predictive control framework
http://hdl.handle.net/2117/111780
System reliability aware model predictive control framework
Salazar Cortés, Jean Carlo; Weber, Philipe; Nejjari AkhiElarab, Fatiha; Sarrate Estruch, Ramon; Theilliol, Didier
This paper presents a Model Predictive Control (MPC) framework taking into account the usage of the actuators to preserve system reliability while maximizing control performance. Two approaches are proposed to preserve system reliability: a global approach that integrates in the control algorithm a representation of system reliability, and a local approach that integrates a representation of component reliability. The tradeoff between the system reliability and the control performance should be taken into account. A methodology for MPC tuning is proposed to handle this tradeoff. System and component reliability are computed based on Dynamic Bayesian Network. The effectiveness and benefits of the proposed control framework are discussed through its application to an overactuated system.
20171212T13:11:41Z
Salazar Cortés, Jean Carlo
Weber, Philipe
Nejjari AkhiElarab, Fatiha
Sarrate Estruch, Ramon
Theilliol, Didier
This paper presents a Model Predictive Control (MPC) framework taking into account the usage of the actuators to preserve system reliability while maximizing control performance. Two approaches are proposed to preserve system reliability: a global approach that integrates in the control algorithm a representation of system reliability, and a local approach that integrates a representation of component reliability. The tradeoff between the system reliability and the control performance should be taken into account. A methodology for MPC tuning is proposed to handle this tradeoff. System and component reliability are computed based on Dynamic Bayesian Network. The effectiveness and benefits of the proposed control framework are discussed through its application to an overactuated system.

Zonotopic fault estimation filter design for discretetime descriptor systems
http://hdl.handle.net/2117/111333
Zonotopic fault estimation filter design for discretetime descriptor systems
Wang, Ye; Wang, Zhenhua; Puig Cayuela, Vicenç; Cembrano Gennari, Gabriela
This paper considers actuatorfault estimation for discretetime descriptor systems with unknown but bounded system disturbance and measurement noise. A zonotopic fault estimation filter is designed based on the analysis of fault detectability indexes. To ensure estimation accuracy, the filter gain in the zonotopic fault estimation filter is optimized through the zonotope minimization. The designed zonotopic filter not only can estimate fault magnitudes, but it also provides fault estimation results in an interval, i.e. the upper and lower bounds of fault magnitudes. Moreover, the proposed fault estimation filter has a nonsingular structure and hence is easy to implement. Finally, simulation results are provided to illustrate the effectiveness of the proposed method.
20171129T12:56:31Z
Wang, Ye
Wang, Zhenhua
Puig Cayuela, Vicenç
Cembrano Gennari, Gabriela
This paper considers actuatorfault estimation for discretetime descriptor systems with unknown but bounded system disturbance and measurement noise. A zonotopic fault estimation filter is designed based on the analysis of fault detectability indexes. To ensure estimation accuracy, the filter gain in the zonotopic fault estimation filter is optimized through the zonotope minimization. The designed zonotopic filter not only can estimate fault magnitudes, but it also provides fault estimation results in an interval, i.e. the upper and lower bounds of fault magnitudes. Moreover, the proposed fault estimation filter has a nonsingular structure and hence is easy to implement. Finally, simulation results are provided to illustrate the effectiveness of the proposed method.

Robust optimization based energy dispatch in smart grids considering simultaneously multiple uncertainties: load demands and energy prices
http://hdl.handle.net/2117/111331
Robust optimization based energy dispatch in smart grids considering simultaneously multiple uncertainties: load demands and energy prices
Nassourou, M; Puig Cayuela, Vicenç; Blesa Izquierdo, Joaquim
Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart microgrid. The smart grid components are described using controloriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a nonlinear nonconvex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in singlelayer and twolayer approaches was also carried out based on the daily cost of energy production.
20171129T12:34:38Z
Nassourou, M
Puig Cayuela, Vicenç
Blesa Izquierdo, Joaquim
Solving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart microgrid. The smart grid components are described using controloriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a nonlinear nonconvex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in singlelayer and twolayer approaches was also carried out based on the daily cost of energy production.

A distributed predictive control approach for periodic flowbased networks: application to drinking water systems
http://hdl.handle.net/2117/110930
A distributed predictive control approach for periodic flowbased networks: application to drinking water systems
Grosso Perez, Juan Manuel; OcampoMartínez, Carlos; Puig Cayuela, Vicenç
This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flowbased networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (alltoall) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of nonsparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a largescale complex flowbased network: the Barcelona drinking water supply system.
20171120T15:30:05Z
Grosso Perez, Juan Manuel
OcampoMartínez, Carlos
Puig Cayuela, Vicenç
This paper proposes a distributed model predictive control approach designed to work in a cooperative manner for controlling flowbased networks showing periodic behaviours. Under this distributed approach, local controllers cooperate in order to enhance the performance of the whole flow network avoiding the use of a coordination layer. Alternatively, controllers use both the monolithic model of the network and the given global cost function to optimise the control inputs of the local controllers but taking into account the effect of their decisions over the remainder subsystems conforming the entire network. In this sense, a global (alltoall) communication strategy is considered. Although the Pareto optimality cannot be reached due to the existence of nonsparse coupling constraints, the asymptotic convergence to a Nash equilibrium is guaranteed. The resultant strategy is tested and its effectiveness is shown when applied to a largescale complex flowbased network: the Barcelona drinking water supply system.

Leak localization in water distribution networks using Bayesian classifiers
http://hdl.handle.net/2117/110151
Leak localization in water distribution networks using Bayesian classifiers
Soldevila Coma, Adrià; Fernández Canti, Rosa M.; Blesa Izquierdo, Joaquim; Tornil Sin, Sebastián; Puig Cayuela, Vicenç
This paper presents a method for leak localization in water distribution networks (WDNs) based on Bayesian classifiers. Probability density functions for pressure residuals are calibrated offline for all the possible leak scenarios by using a hydraulic simulator, and considering the leak size uncertainty, demand uncertainty and sensor noise. A Bayesian classifier is applied online to the computed residuals to determine the location of leaks in the WDN. A time horizon based reasoning combined with the Bayesian classifier is also proposed to improve the localization accuracy. Two case studies based on the Hanoi and the Nova Icària networks are used to illustrate the performance of the proposed approach. Simulation results are presented for the Hanoi case study, whereas results for a real leak scenario are shown for the Nova Icària case study.
20171108T14:00:50Z
Soldevila Coma, Adrià
Fernández Canti, Rosa M.
Blesa Izquierdo, Joaquim
Tornil Sin, Sebastián
Puig Cayuela, Vicenç
This paper presents a method for leak localization in water distribution networks (WDNs) based on Bayesian classifiers. Probability density functions for pressure residuals are calibrated offline for all the possible leak scenarios by using a hydraulic simulator, and considering the leak size uncertainty, demand uncertainty and sensor noise. A Bayesian classifier is applied online to the computed residuals to determine the location of leaks in the WDN. A time horizon based reasoning combined with the Bayesian classifier is also proposed to improve the localization accuracy. Two case studies based on the Hanoi and the Nova Icària networks are used to illustrate the performance of the proposed approach. Simulation results are presented for the Hanoi case study, whereas results for a real leak scenario are shown for the Nova Icària case study.

Distributed zonotopic setmembership state estimation based on optimization methods with partial projection
http://hdl.handle.net/2117/110150
Distributed zonotopic setmembership state estimation based on optimization methods with partial projection
Álamo Cantarero, Teodoro; Wang, Ye; Puig Cayuela, Vicenç; Cembrano Gennari, Gabriela
A distributed setmembership approach is proposed for the state estimation of largescale systems. The uncertain system states are bounded in a sequence of the distributed setmembership estimators considering unknownbutbounded system disturbances and measurement noise. In the framework of the setmembership approach, the measurement consistency test is implemented by finding parameterized intersection zonotopes. The size of the intersection zonotope is minimized by solving an optimization problem including a sequence of linear/bilinear matrix inequalities based on the weighted 2norm criterion of the generator matrix. Meanwhile, for the distributed setmembership estimators, the partial projection method is considered to correct the estimation of the neighbor state. On the other hand, an online method is also provided. Finally, the proposed distributed setmembership approach is verified in a case study based on a urban drainage network.
20171108T13:57:33Z
Álamo Cantarero, Teodoro
Wang, Ye
Puig Cayuela, Vicenç
Cembrano Gennari, Gabriela
A distributed setmembership approach is proposed for the state estimation of largescale systems. The uncertain system states are bounded in a sequence of the distributed setmembership estimators considering unknownbutbounded system disturbances and measurement noise. In the framework of the setmembership approach, the measurement consistency test is implemented by finding parameterized intersection zonotopes. The size of the intersection zonotope is minimized by solving an optimization problem including a sequence of linear/bilinear matrix inequalities based on the weighted 2norm criterion of the generator matrix. Meanwhile, for the distributed setmembership estimators, the partial projection method is considered to correct the estimation of the neighbor state. On the other hand, an online method is also provided. Finally, the proposed distributed setmembership approach is verified in a case study based on a urban drainage network.

Solving Diagnosability of Hybrid Systems via Abstraction and Discrete Event Techniques
http://hdl.handle.net/2117/110149
Solving Diagnosability of Hybrid Systems via Abstraction and Discrete Event Techniques
Grastien, Alban; TravéMassuyès, Louise; Puig Cayuela, Vicenç
This paper addresses the problem of determining the diagnosability of hybrid systems by abstracting hybrid models to a discrete event setting. From the continuous model the abstraction only remembers two pieces of information: indiscernability between modes (when they are guaranteed to generate different observations) and ephemerality (when the system cannot stay forever in a given set of modes). Then, we use standard discrete event system diagnosability algorithms. The second contribution is an iterative approach to diagnosability that starts from the most abstract discrete event model of the hybrid system. If it is diagnosable, that means that the hybrid system is diagnosable. If it is not, the counterexample generated by the diagnosability procedure is analysed to refine the DES. If no refinement is found, then it can not be proved that the hybrid system is diagnosable. Otherwise, the refinement is included in the abstract DES model and the diagnosability procedure continues.
20171108T13:26:25Z
Grastien, Alban
TravéMassuyès, Louise
Puig Cayuela, Vicenç
This paper addresses the problem of determining the diagnosability of hybrid systems by abstracting hybrid models to a discrete event setting. From the continuous model the abstraction only remembers two pieces of information: indiscernability between modes (when they are guaranteed to generate different observations) and ephemerality (when the system cannot stay forever in a given set of modes). Then, we use standard discrete event system diagnosability algorithms. The second contribution is an iterative approach to diagnosability that starts from the most abstract discrete event model of the hybrid system. If it is diagnosable, that means that the hybrid system is diagnosable. If it is not, the counterexample generated by the diagnosability procedure is analysed to refine the DES. If no refinement is found, then it can not be proved that the hybrid system is diagnosable. Otherwise, the refinement is included in the abstract DES model and the diagnosability procedure continues.

Iterative learning control experimental results in twinrotor device
http://hdl.handle.net/2117/110132
Iterative learning control experimental results in twinrotor device
Mascaró Palliser, Ruben; Costa Castelló, Ramon; Ramos Fuentes, German A.
This paper presents the results of applying the Iterative Learning Control algorithms to a TwinRotor MultipleInput MultipleOutput System (TRMS) in order to achieve high performance in repetitive tracking of trajectories. The plant, which is similar to a prototype of helicopter, is characterized by its highly nonlinear and crosscoupled dynamics. In the first phase, the system is modelled using the Lagrangian approach and combining theoretical and experimental results. Thereafter, a hierarchical control architecture which combines a baseline feedback controller with an Iterative Learning Control algorithm is developed. Finally, the responses of the real device and a complete analysis of the learning behaviour are exposed.
20171108T09:59:13Z
Mascaró Palliser, Ruben
Costa Castelló, Ramon
Ramos Fuentes, German A.
This paper presents the results of applying the Iterative Learning Control algorithms to a TwinRotor MultipleInput MultipleOutput System (TRMS) in order to achieve high performance in repetitive tracking of trajectories. The plant, which is similar to a prototype of helicopter, is characterized by its highly nonlinear and crosscoupled dynamics. In the first phase, the system is modelled using the Lagrangian approach and combining theoretical and experimental results. Thereafter, a hierarchical control architecture which combines a baseline feedback controller with an Iterative Learning Control algorithm is developed. Finally, the responses of the real device and a complete analysis of the learning behaviour are exposed.

Temperature control of opencathode PEM fuel cells
http://hdl.handle.net/2117/110131
Temperature control of opencathode PEM fuel cells
Strahl, Stephan; Costa Castelló, Ramon
Proper temperature control of Proton Exchange Membrane (PEM) Fuel Cells is a crucial factor for optimizing fuel cell performance. A robust temperature controller is required for optimal water management of PEM fuel cells. This paper describes a modelbased characterization of the equilibrium points of an opencathode fuel cell system as the baseline for proper controller design, highlighting the relation between fuel cell temperature, humidification and performance. Phase plane analysis of the nonlinear model versus a linearized model around different points of operation shows the potential of approximating the nonlinear system behavior with a linear model. The methodology for the system analysis presented in this paper finally serves for the development of control schemes using robust control techniques. The designed controller is validated in simulation with the nonlinear plant model.
20171108T09:26:32Z
Strahl, Stephan
Costa Castelló, Ramon
Proper temperature control of Proton Exchange Membrane (PEM) Fuel Cells is a crucial factor for optimizing fuel cell performance. A robust temperature controller is required for optimal water management of PEM fuel cells. This paper describes a modelbased characterization of the equilibrium points of an opencathode fuel cell system as the baseline for proper controller design, highlighting the relation between fuel cell temperature, humidification and performance. Phase plane analysis of the nonlinear model versus a linearized model around different points of operation shows the potential of approximating the nonlinear system behavior with a linear model. The methodology for the system analysis presented in this paper finally serves for the development of control schemes using robust control techniques. The designed controller is validated in simulation with the nonlinear plant model.

Noncentralized control for flowbased distribution networks: a gametheoretical insight
http://hdl.handle.net/2117/109821
Noncentralized control for flowbased distribution networks: a gametheoretical insight
Barreiro Gómez, Julian; OcampoMartínez, Carlos; Quijano Silva, Nicanor; Maestre Torreblanca, José María
This paper solves a datadriven control problem for a flowbased distribution network with two objectives: a resource allocation and a fair distribution of costs. These objectives represent both cooperation and competition directions. It is proposed a solution that combines either a centralized or distributed cooperative game approach using the Shapley value to determine a proper partitioning of the system and a fair communication cost distribution. On the other hand, a decentralized noncooperative game approach computing the Nash equilibrium is used to achieve the control objective of the resource allocation under a noncomplete information topology. Furthermore, an invariantset property is presented and the closedloop system stability is analyzed for the noncooperative game approach. Another contribution regarding the cooperative game approach is an alternative way to compute the Shapley value for the proposed specific characteristic function. Unlike the classical cooperativegames approach, which has a limited application due to the combinatorial explosion issues, the alternative method allows calculating the Shapley value in polynomial time and hence can be applied to largescale problems.
© <year>. This manuscript version is made available under the CCBYNCND 4.0 license http://creativecommons.org/licenses/byncnd/4.0/
20171106T08:55:03Z
Barreiro Gómez, Julian
OcampoMartínez, Carlos
Quijano Silva, Nicanor
Maestre Torreblanca, José María
This paper solves a datadriven control problem for a flowbased distribution network with two objectives: a resource allocation and a fair distribution of costs. These objectives represent both cooperation and competition directions. It is proposed a solution that combines either a centralized or distributed cooperative game approach using the Shapley value to determine a proper partitioning of the system and a fair communication cost distribution. On the other hand, a decentralized noncooperative game approach computing the Nash equilibrium is used to achieve the control objective of the resource allocation under a noncomplete information topology. Furthermore, an invariantset property is presented and the closedloop system stability is analyzed for the noncooperative game approach. Another contribution regarding the cooperative game approach is an alternative way to compute the Shapley value for the proposed specific characteristic function. Unlike the classical cooperativegames approach, which has a limited application due to the combinatorial explosion issues, the alternative method allows calculating the Shapley value in polynomial time and hence can be applied to largescale problems.