IRI  Institut de Robòtica i Informàtica Industrial, CSICUPC
http://hdl.handle.net/2117/2249
20171211T04:22:57Z

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.

Periodic nonlinear economic model predictive control with changing horizon for water distribution networks
http://hdl.handle.net/2117/111073
Periodic nonlinear economic model predictive control with changing horizon for water distribution networks
Wang, Ye; Salvador, José Ramón; Muñoz De la Peña, David; Puig Cayuela, Vicenç; Cembrano Gennari, Gabriela
A periodic nonlinear economic model predictive control (EMPC) with changing prediction horizon is proposed for the optimal management of water distribution networks (WDNs). The control model of the WDN is built by means of nonlinear differentialalgebraic equations in which both the hydraulic pressure and flow variables are taken into account. The model allows the controller to consider minimum pressure constraints at the demands. A periodic terminal constraint is employed in order to guarantee closedloop stability. The prediction horizon is modified online in order to guarantee convergence to the optimal periodic trajectory. The proposed control strategy is verified with the case study of the Richmond water network in a realistic hydraulic simulator. Although there are modeling errors between the control model and hydraulic model, the closedloop system converges to a suboptimal periodic trajectory satisfying all the constraints.
20171122T13:02:17Z
Wang, Ye
Salvador, José Ramón
Muñoz De la Peña, David
Puig Cayuela, Vicenç
Cembrano Gennari, Gabriela
A periodic nonlinear economic model predictive control (EMPC) with changing prediction horizon is proposed for the optimal management of water distribution networks (WDNs). The control model of the WDN is built by means of nonlinear differentialalgebraic equations in which both the hydraulic pressure and flow variables are taken into account. The model allows the controller to consider minimum pressure constraints at the demands. A periodic terminal constraint is employed in order to guarantee closedloop stability. The prediction horizon is modified online in order to guarantee convergence to the optimal periodic trajectory. The proposed control strategy is verified with the case study of the Richmond water network in a realistic hydraulic simulator. Although there are modeling errors between the control model and hydraulic model, the closedloop system converges to a suboptimal periodic trajectory satisfying all the constraints.

Word ordering and document adjacency for large loop closure detection in 2D laser maps
http://hdl.handle.net/2117/110932
Word ordering and document adjacency for large loop closure detection in 2D laser maps
Deray, Jeremie; Solà Ortega, Joan; AndradeCetto, Juan
We address in this paper the problem of loop closure detection for laserbased simultaneous localization and mapping (SLAM) of very large areas. Consistent with the state of the art, the map is encoded as a graph of poses, and to cope with very large mapping capabilities, loop closures are asserted by comparing the features extracted from a query laser scan against a previously acquired corpus of scan features using a bagofwords (BoW) scheme. Two contributions are here presented. First, to benefit from the graph topology, feature frequency scores in the BoW are computed not only for each individual scan but also from neighboring scans in the SLAM graph. This has the effect of enforcing neighbor relational information during document matching. Secondly, a weak geometric check that takes into account feature ordering and occlusions is introduced that substantially improves loop closure detection performance. The two contributions are evaluated both separately and jointly on four common SLAM datasets, and are shown to improve the stateoftheart performance both in terms of precision and recall in most of the cases. Moreover, our current implementation is designed to work at nearly frame rate, allowing loop closure query resolution at nearly 22 Hz for the best case scenario and 2 Hz for the worst case scenario.
© 20xx 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
20171120T15:39:15Z
Deray, Jeremie
Solà Ortega, Joan
AndradeCetto, Juan
We address in this paper the problem of loop closure detection for laserbased simultaneous localization and mapping (SLAM) of very large areas. Consistent with the state of the art, the map is encoded as a graph of poses, and to cope with very large mapping capabilities, loop closures are asserted by comparing the features extracted from a query laser scan against a previously acquired corpus of scan features using a bagofwords (BoW) scheme. Two contributions are here presented. First, to benefit from the graph topology, feature frequency scores in the BoW are computed not only for each individual scan but also from neighboring scans in the SLAM graph. This has the effect of enforcing neighbor relational information during document matching. Secondly, a weak geometric check that takes into account feature ordering and occlusions is introduced that substantially improves loop closure detection performance. The two contributions are evaluated both separately and jointly on four common SLAM datasets, and are shown to improve the stateoftheart performance both in terms of precision and recall in most of the cases. Moreover, our current implementation is designed to work at nearly frame rate, allowing loop closure query resolution at nearly 22 Hz for the best case scenario and 2 Hz for the worst case scenario.

Kinodynamic planning on constraint manifolds
http://hdl.handle.net/2117/110931
Kinodynamic planning on constraint manifolds
Bordalba Llaberia, Ricard; Porta Pleite, Josep Maria; Ros Giralt, Lluís
This report presents a motion planner for systems subject to kinematic and dynamic constraints. The former appear when kinematic loops are present in the system, such as in parallel manipulators, in robots that cooperate to achieve a given task, or in situations involving contacts with the environment. The latter are necessary to obtain realistic trajectories, taking into account the forces acting on the system. The kinematic constraints make the state space become an implicitlydefined manifold, which complicates the application of common motion planning techniques. To address this issue, the planner constructs an atlas of the state space manifold incrementally, and uses this atlas both to generate random states and to dynamically simulate the steering of the system towards such states. The resulting tools are then exploited to construct a rapidlyexploring random tree (RRT) over the state space. To the best of our knowledge, this is the first randomized kinodynamic planner for implicitlydefined state spaces. The test cases presented validate the approach in significantlycomplex systems.
20171120T15:33:22Z
Bordalba Llaberia, Ricard
Porta Pleite, Josep Maria
Ros Giralt, Lluís
This report presents a motion planner for systems subject to kinematic and dynamic constraints. The former appear when kinematic loops are present in the system, such as in parallel manipulators, in robots that cooperate to achieve a given task, or in situations involving contacts with the environment. The latter are necessary to obtain realistic trajectories, taking into account the forces acting on the system. The kinematic constraints make the state space become an implicitlydefined manifold, which complicates the application of common motion planning techniques. To address this issue, the planner constructs an atlas of the state space manifold incrementally, and uses this atlas both to generate random states and to dynamically simulate the steering of the system towards such states. The resulting tools are then exploited to construct a rapidlyexploring random tree (RRT) over the state space. To the best of our knowledge, this is the first randomized kinodynamic planner for implicitlydefined state spaces. The test cases presented validate the approach in significantlycomplex systems.

ROS wrapper for realtime multiperson pose estimation with a single camera
http://hdl.handle.net/2117/110929
ROS wrapper for realtime multiperson pose estimation with a single camera
Arduengo García, Miguel; Jorgensen, Steven Jens; Hambuchen, Kimberly; Sentis, Luis; MorenoNoguer, Francesc; Alenyà Ribas, Guillem
For robots to be deployable in human occupied environments, the robots must have humanawareness and generate humanaware behaviors and policies. OpenPose is a library for realtime multiperson keypoint detection. We have considered the implementation of a ROS package that would allow the estimation of 2d pose from simple RGB images, for which we have introduced a ROS wrapper that automatically recovers the pose of several people from a single camera using OpenPose. Additionally, a ROS node to obtain 3d pose estimation from the initial 2d pose estimation when a depth image is synchronized with the RGB image (RGBD image, such as with a Kinect camera) has been developed. This aim is attained projecting the 2d pose estimation onto the pointcloud of the depth image.
20171120T15:26:26Z
Arduengo García, Miguel
Jorgensen, Steven Jens
Hambuchen, Kimberly
Sentis, Luis
MorenoNoguer, Francesc
Alenyà Ribas, Guillem
For robots to be deployable in human occupied environments, the robots must have humanawareness and generate humanaware behaviors and policies. OpenPose is a library for realtime multiperson keypoint detection. We have considered the implementation of a ROS package that would allow the estimation of 2d pose from simple RGB images, for which we have introduced a ROS wrapper that automatically recovers the pose of several people from a single camera using OpenPose. Additionally, a ROS node to obtain 3d pose estimation from the initial 2d pose estimation when a depth image is synchronized with the RGB image (RGBD image, such as with a Kinect camera) has been developed. This aim is attained projecting the 2d pose estimation onto the pointcloud of the depth image.

Dual REPS: a generalization of relative entropy policy search exploiting bad experiences
http://hdl.handle.net/2117/110925
Dual REPS: a generalization of relative entropy policy search exploiting bad experiences
Colomé Figueras, Adrià; Torras, Carme
Policy search (PS) algorithms are widely used for their simplicity and effectiveness in finding solutions for robotic problems. However, most current PS algorithms derive policies by statistically fitting the data from the best experiments only. This means that experiments yielding a poor performance are usually discarded or given too little influence on the policy update. In this paper, we propose a generalization of the relative entropy policy search (REPS) algorithm that takes bad experiences into consideration when computing a policy. The proposed approach, named dual REPS (DREPS) following the philosophical interpretation of the duality between good and bad, finds clusters of experimental data yielding a poor behavior and adds them to the optimization problem as a repulsive constraint. Thus, considering that there is a duality between good and bad data samples, both are taken into account in the stochastic search for a policy. Additionally, a cluster with the best samples may be included as an attractor to enforce faster convergence to a single optimal solution in multimodal problems. We first tested our proposed approach in a simulated reinforcement learning setting and found that DREPS considerably speeds up the learning process, especially during the early optimization steps and in cases where other approaches get trapped in between several alternative maxima. Further experiments in which a real robot had to learn a task with a multimodal reward function confirm the advantages of our proposed approach with respect to REPS.
© 20xx 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.
20171120T15:14:58Z
Colomé Figueras, Adrià
Torras, Carme
Policy search (PS) algorithms are widely used for their simplicity and effectiveness in finding solutions for robotic problems. However, most current PS algorithms derive policies by statistically fitting the data from the best experiments only. This means that experiments yielding a poor performance are usually discarded or given too little influence on the policy update. In this paper, we propose a generalization of the relative entropy policy search (REPS) algorithm that takes bad experiences into consideration when computing a policy. The proposed approach, named dual REPS (DREPS) following the philosophical interpretation of the duality between good and bad, finds clusters of experimental data yielding a poor behavior and adds them to the optimization problem as a repulsive constraint. Thus, considering that there is a duality between good and bad data samples, both are taken into account in the stochastic search for a policy. Additionally, a cluster with the best samples may be included as an attractor to enforce faster convergence to a single optimal solution in multimodal problems. We first tested our proposed approach in a simulated reinforcement learning setting and found that DREPS considerably speeds up the learning process, especially during the early optimization steps and in cases where other approaches get trapped in between several alternative maxima. Further experiments in which a real robot had to learn a task with a multimodal reward function confirm the advantages of our proposed approach with respect to REPS.

Economic model predictive control for energy dispatch of a smart microgrid system
http://hdl.handle.net/2117/110670
Economic model predictive control for energy dispatch of a smart microgrid system
Nassourou, M; Puig Cayuela, Vicenç; Blesa Izquierdo, Joaquim; OcampoMartínez, Carlos
The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently solved using classical control or adhoc methods. This paper proposes the application of Economic Model Predictive Control (EMPC) for the management of a smart microgrid system connected to an electrical power grid. The system comprises several subsystems, namely some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and
some storage devices (batteries). The batteries are charged with
the energy from the PV panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Assuming the load demand and the energy prices to be known, this study shows that EMPC is economically superior to other Model Predictive Control (MPC) based strategies (a
standard tracking MPC, and their cascaded version in form of hierarchical twolayer approach).
© 2017 IEEE. Personal use of this ma terial is permitted. Permission from IEEE must be obtained for al l other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, f or resale or redistribution to se rvers or lists, or reuse of any copyrighted compone nt of this work in other works
20171115T11:20:41Z
Nassourou, M
Puig Cayuela, Vicenç
Blesa Izquierdo, Joaquim
OcampoMartínez, Carlos
The problem of energy dispatch in heterogeneous complex systems such as smart grids cannot be efficiently solved using classical control or adhoc methods. This paper proposes the application of Economic Model Predictive Control (EMPC) for the management of a smart microgrid system connected to an electrical power grid. The system comprises several subsystems, namely some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and
some storage devices (batteries). The batteries are charged with
the energy from the PV panels, wind and hydroelectric generators, and they are discharged whenever the generators produce less energy than needed. The subsystems are interconnected via a DC Bus, from which load demands are satisfied. Assuming the load demand and the energy prices to be known, this study shows that EMPC is economically superior to other Model Predictive Control (MPC) based strategies (a
standard tracking MPC, and their cascaded version in form of hierarchical twolayer approach).

Towards SLAM with an eventsbased camera
http://hdl.handle.net/2117/110514
Towards SLAM with an eventsbased camera
Clavera Gilaberte, Ignasi; Solà Ortega, Joan; AndradeCetto, Juan
Eventbased cameras have an incredible potential in realtime and realworld robotics. They would enable more efficient algorithms in applications where high demanding requirements, such as rapid dynamic motion and high dynamic range, make standard cameras run into problems rapid dynamic motion and high dynamic range. While traditional cameras are based in the framebase paradigm  a shutter captures a certain amount of pictures per second , the bioinspired event cameras have pixels that respond independently to the change of logintensity generating asynchronous events. An special appeal for this type of cameras is their low bandwidth, since the stream of events contain all the information getting rid of the redundancy. This sensors that mimic some properties of the human retina has microseconds latency and 120 dB dynamic range (in contrast to the 60 dB of the standard cameras).
However, the current impact of the event cameras has been tiny due to the necessity of completely new algorithm, there is no global measurement of the intensity which would allow the use of current methods. The fact that an event corresponds to an asynchronous local intensity difference turns out to be a challenging problem if one wants to recover the motion as well as the scene. This article tries to illustrate the several problems that are needed to face when dealing with this problem and some of the different approaches taken.
First of all, we will explain the generative model of the event camera and the preliminaries, followed by the different approaches. Finally will the conclusions and a glossary of the code.
20171113T17:25:37Z
Clavera Gilaberte, Ignasi
Solà Ortega, Joan
AndradeCetto, Juan
Eventbased cameras have an incredible potential in realtime and realworld robotics. They would enable more efficient algorithms in applications where high demanding requirements, such as rapid dynamic motion and high dynamic range, make standard cameras run into problems rapid dynamic motion and high dynamic range. While traditional cameras are based in the framebase paradigm  a shutter captures a certain amount of pictures per second , the bioinspired event cameras have pixels that respond independently to the change of logintensity generating asynchronous events. An special appeal for this type of cameras is their low bandwidth, since the stream of events contain all the information getting rid of the redundancy. This sensors that mimic some properties of the human retina has microseconds latency and 120 dB dynamic range (in contrast to the 60 dB of the standard cameras).
However, the current impact of the event cameras has been tiny due to the necessity of completely new algorithm, there is no global measurement of the intensity which would allow the use of current methods. The fact that an event corresponds to an asynchronous local intensity difference turns out to be a challenging problem if one wants to recover the motion as well as the scene. This article tries to illustrate the several problems that are needed to face when dealing with this problem and some of the different approaches taken.
First of all, we will explain the generative model of the event camera and the preliminaries, followed by the different approaches. Finally will the conclusions and a glossary of the code.

Identification of PEM fuel cells based on support vector regression and orthonormal bases
http://hdl.handle.net/2117/110513
Identification of PEM fuel cells based on support vector regression and orthonormal bases
Feroldi, Diego Hernan; Gómez, Juan Carlos; Roda Serrat, Vicente
Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis. In this paper, a new Wiener model identification method based on Support Vector (SV) Regression and orthonormal bases is introduced and used to estimate a nonlinear dynamical model for the air supply system of a laboratory PEMFC from experimental data. The method is experimentally validated using a PEMFC system based on a ZB 8cell stack with Nafion 115 membrane electrode assemblies
© 20xx 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.
20171113T17:22:39Z
Feroldi, Diego Hernan
Gómez, Juan Carlos
Roda Serrat, Vicente
Polymer Electrolyte Membrane Fuel Cells (PEMFC) are efficient devices that convert the chemical energy of the reactants in electricity. In this type of fuel cells, the performance of the air supply system is fundamental to improve their efficiency. An accurate mathematical model representing the air filling dynamics for a wide range of operating points is then necessary for control design and analysis. In this paper, a new Wiener model identification method based on Support Vector (SV) Regression and orthonormal bases is introduced and used to estimate a nonlinear dynamical model for the air supply system of a laboratory PEMFC from experimental data. The method is experimentally validated using a PEMFC system based on a ZB 8cell stack with Nafion 115 membrane electrode assemblies