Capítols de llibre
http://hdl.handle.net/2117/3934
2017-04-23T21:44:36ZIK-FA, a new heuristic inverse kinematics solver using firefly algorithm
http://hdl.handle.net/2117/103030
IK-FA, a new heuristic inverse kinematics solver using firefly algorithm
Rokbani, Nizar; Casals Gelpi, Alicia; Alimi, Adel M.
In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, ß, ¿, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10-3 seconds with a position error fitness around 3.116 × 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10-9.
2017-03-29T10:48:02ZRokbani, NizarCasals Gelpi, AliciaAlimi, Adel M.In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, ß, ¿, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10-3 seconds with a position error fitness around 3.116 × 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10-9.Proyecto transversal en los estudios de Ingeniería Electrónica Industrial y Automática
http://hdl.handle.net/2117/102700
Proyecto transversal en los estudios de Ingeniería Electrónica Industrial y Automática
Martínez García, Herminio; Domingo Peña, Joan; Durán Moyano, José L.
2017-03-21T09:31:20ZMartínez García, HerminioDomingo Peña, JoanDurán Moyano, José L.Aprendizaje cooperativo y Flipped Classroom
http://hdl.handle.net/2117/102698
Aprendizaje cooperativo y Flipped Classroom
Domingo Peña, Joan; Durán Moyano, José L.; Martínez García, Herminio
2017-03-21T09:28:03ZDomingo Peña, JoanDurán Moyano, José L.Martínez García, HerminioEl lío de las siglas
http://hdl.handle.net/2117/102696
El lío de las siglas
Domingo Peña, Joan; Durán Moyano, José L.; Martínez García, Herminio
2017-03-21T09:24:36ZDomingo Peña, JoanDurán Moyano, José L.Martínez García, HerminioSliding mode control of LCL full-bridge rectifiers
http://hdl.handle.net/2117/90439
Sliding mode control of LCL full-bridge rectifiers
Dòria Cerezo, Arnau; Biel Solé, Domingo; Fossas Colet, Enric
In control theory, sliding mode control, or SMC, is a nonlinear control method that alters the dynamics of a nonlinear system by application of a discontinuous control signal that forces the system to "slide" along a crosssection of the system's normal behavior. This book describes recent advances in the theory, properties, methods and applications of SMC. The book is organised into four parts. The first part is devoted to the design of higher-order sliding-mode controllers, with specific designs presented in the context of disturbance rejection by means of observation and identification. The second part offers a set of tools for establishing different dynamic properties of systems with discontinuous right-hand sides. Time discretization is addressed in the third part. First-order sliding modes are discretized using an implicit scheme - higher-order slidingmode differentiators, typically used in output-feedback schemes, are discretized in such a way that the optimal accuracy of their continuous-time counterparts is restored. The last part is dedicated to applications. In the context of energy conversion, sliding-mode control is applied to variable-speed wind turbines, fuel cell coupled to a power converter, rugged DC series motors and rectifiers with unity power factor, and electropneumatic actuator. Finally, an event-triggered sliding-mode scheme is proposed for networked control systems subject to packet loss, jitter and delayed transmissions.
2016-10-04T10:29:07ZDòria Cerezo, ArnauBiel Solé, DomingoFossas Colet, EnricIn control theory, sliding mode control, or SMC, is a nonlinear control method that alters the dynamics of a nonlinear system by application of a discontinuous control signal that forces the system to "slide" along a crosssection of the system's normal behavior. This book describes recent advances in the theory, properties, methods and applications of SMC. The book is organised into four parts. The first part is devoted to the design of higher-order sliding-mode controllers, with specific designs presented in the context of disturbance rejection by means of observation and identification. The second part offers a set of tools for establishing different dynamic properties of systems with discontinuous right-hand sides. Time discretization is addressed in the third part. First-order sliding modes are discretized using an implicit scheme - higher-order slidingmode differentiators, typically used in output-feedback schemes, are discretized in such a way that the optimal accuracy of their continuous-time counterparts is restored. The last part is dedicated to applications. In the context of energy conversion, sliding-mode control is applied to variable-speed wind turbines, fuel cell coupled to a power converter, rugged DC series motors and rectifiers with unity power factor, and electropneumatic actuator. Finally, an event-triggered sliding-mode scheme is proposed for networked control systems subject to packet loss, jitter and delayed transmissions.DSP-based natural frame control schemes for three-phase unity-power-factor rectifiers
http://hdl.handle.net/2117/87219
DSP-based natural frame control schemes for three-phase unity-power-factor rectifiers
Guzmán Solà, Ramon; García de Vicuña Muñoz de la Nava, José Luis; Pena Alzola, Rafael
Traditionally, digital signal processor (DSP) control algorithms for three-phase power converters are designed in rotating or stationary reference frames. These approaches require the use of rotation matrices and employ linear controllers such as proportional integral (PI) and proportional resonant. This chapter presents an alternative control solution developed in the natural reference frame and applied to a three-phase unity power factor rectifier (UPFR). This solution requires no transformation matrices, and by harnessing all computation capabilities of the modern DSPs, nonlinear techniques, such as sliding-mode control (SMC) [1, 2] and Kalman filter (KF) [3], can be employed. The main features, along with the advantages and the limitations, of this approach will be discussed in detail throughout the chapter.
2016-05-20T10:25:31ZGuzmán Solà, RamonGarcía de Vicuña Muñoz de la Nava, José LuisPena Alzola, RafaelTraditionally, digital signal processor (DSP) control algorithms for three-phase power converters are designed in rotating or stationary reference frames. These approaches require the use of rotation matrices and employ linear controllers such as proportional integral (PI) and proportional resonant. This chapter presents an alternative control solution developed in the natural reference frame and applied to a three-phase unity power factor rectifier (UPFR). This solution requires no transformation matrices, and by harnessing all computation capabilities of the modern DSPs, nonlinear techniques, such as sliding-mode control (SMC) [1, 2] and Kalman filter (KF) [3], can be employed. The main features, along with the advantages and the limitations, of this approach will be discussed in detail throughout the chapter.MPC framework for system reliability optimization
http://hdl.handle.net/2117/86091
MPC framework for system reliability optimization
Salazar Cortés, Jean Carlo; Weber, Philipe; Nejjari Akhi-Elarab, Fatiha; Theilliol, Didier; Sarrate Estruch, Ramon
This book is devoted to the demands of research and industrial centers for diagnostics, monitoring and decision making systems that result from the increasing complexity of automation and systems, the need to ensure the highest level of reliability and safety, and continuing research and the development of innovative approaches to fault diagnosis. The contributions combine domains of engineering knowledge for diagnosis, including detection, isolation, localization, identification, reconfiguration and fault-tolerant control.
The book is divided into six parts: (I) Fault Detection and Isolation; (II) Estimation and Identification; (III) Robust and Fault Tolerant Control; (IV) Industrial and Medical Diagnostics; (V) Artificial Intelligence; (VI) Expert and Computer Systems.
2016-04-22T09:08:15ZSalazar Cortés, Jean CarloWeber, PhilipeNejjari Akhi-Elarab, FatihaTheilliol, DidierSarrate Estruch, RamonThis book is devoted to the demands of research and industrial centers for diagnostics, monitoring and decision making systems that result from the increasing complexity of automation and systems, the need to ensure the highest level of reliability and safety, and continuing research and the development of innovative approaches to fault diagnosis. The contributions combine domains of engineering knowledge for diagnosis, including detection, isolation, localization, identification, reconfiguration and fault-tolerant control.
The book is divided into six parts: (I) Fault Detection and Isolation; (II) Estimation and Identification; (III) Robust and Fault Tolerant Control; (IV) Industrial and Medical Diagnostics; (V) Artificial Intelligence; (VI) Expert and Computer Systems.Coordinating model predictive control of transport and supply water systems
http://hdl.handle.net/2117/86014
Coordinating model predictive control of transport and supply water systems
Sun, Congcong; Puig Cayuela, Vicenç; Cembrano Gennari, Gabriela
This book aims at stimulating discussion between researchers working on state of the art approaches for operational control and design of transport of water on the one hand and researchers working on state of the art approaches for transport over water on the other hand. The main contribution of the book as a whole is to present novel perspectives ultimately leading to the management of an envisioned unified management framework taking the recent advances from both worlds as a baseline.
The book is intended to be a reference for control-oriented engineers who manage water systems with either or both purposes in mind (transport of water, transport of goods over water). It highlights the possible twofold nature of water projects, where water either acts as primary object of study or as a means. The book is dedicated to comparing and relating to one another different strategies for (operational) management and control of different but strongly related systems in the framework of the water. In that sense, the book presents different approaches treating both the transport of water and transport over water. It compares the different approaches within the same field, highlighting their distinguishing features and advantages according to selected qualitative indices, and demonstrates the interaction and cross-relations between both fields. It will also help to determine the gaps and common points for both fields towards the design of such a unifying framework, which is lacking in the literature. Additionally, the book looks at case studies where the design of modeling/control strategies of either transport of water or transport over water have been proposed, discussed or simulated.
2016-04-20T16:26:33ZSun, CongcongPuig Cayuela, VicençCembrano Gennari, GabrielaThis book aims at stimulating discussion between researchers working on state of the art approaches for operational control and design of transport of water on the one hand and researchers working on state of the art approaches for transport over water on the other hand. The main contribution of the book as a whole is to present novel perspectives ultimately leading to the management of an envisioned unified management framework taking the recent advances from both worlds as a baseline.
The book is intended to be a reference for control-oriented engineers who manage water systems with either or both purposes in mind (transport of water, transport of goods over water). It highlights the possible twofold nature of water projects, where water either acts as primary object of study or as a means. The book is dedicated to comparing and relating to one another different strategies for (operational) management and control of different but strongly related systems in the framework of the water. In that sense, the book presents different approaches treating both the transport of water and transport over water. It compares the different approaches within the same field, highlighting their distinguishing features and advantages according to selected qualitative indices, and demonstrates the interaction and cross-relations between both fields. It will also help to determine the gaps and common points for both fields towards the design of such a unifying framework, which is lacking in the literature. Additionally, the book looks at case studies where the design of modeling/control strategies of either transport of water or transport over water have been proposed, discussed or simulated.Dense segmentation-aware descriptors
http://hdl.handle.net/2117/85171
Dense segmentation-aware descriptors
Trulls Fortuny, Eduard; Kokkinos, Iasonas; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc
Dense descriptors are becoming increasingly popular in a host of tasks, such as dense image correspondence, bag-of-words image classification, and label transfer. However, the extraction of descriptors on generic image points, rather than selecting geometric features, requires rethinking how to achieve invariance to nuisance parameters. In this work we pursue invariance to occlusions and background changes by introducing segmentation information within dense feature construction. The core idea is to use the segmentation cues to downplay the features coming from image areas that are unlikely to belong to the same region as the feature point. We show how to integrate this idea with dense SIFT, as well as with the dense scale- and rotation-invariant descriptor (SID). We thereby deliver dense descriptors that are invariant to background changes, rotation, and/or scaling. We explore the merit of our technique in conjunction with large displacement motion estimation and wide-baseline stereo, and demonstrate that exploiting segmentation information yields clear improvements.
2016-04-05T08:27:39ZTrulls Fortuny, EduardKokkinos, IasonasSanfeliu Cortés, AlbertoMoreno-Noguer, FrancescDense descriptors are becoming increasingly popular in a host of tasks, such as dense image correspondence, bag-of-words image classification, and label transfer. However, the extraction of descriptors on generic image points, rather than selecting geometric features, requires rethinking how to achieve invariance to nuisance parameters. In this work we pursue invariance to occlusions and background changes by introducing segmentation information within dense feature construction. The core idea is to use the segmentation cues to downplay the features coming from image areas that are unlikely to belong to the same region as the feature point. We show how to integrate this idea with dense SIFT, as well as with the dense scale- and rotation-invariant descriptor (SID). We thereby deliver dense descriptors that are invariant to background changes, rotation, and/or scaling. We explore the merit of our technique in conjunction with large displacement motion estimation and wide-baseline stereo, and demonstrate that exploiting segmentation information yields clear improvements.Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant
http://hdl.handle.net/2117/85144
Real-time experimental implementation of predictive control schemes in a small-scale pasteurization plant
Rosich, Albert; Ocampo-Martínez, Carlos
Model predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.
2016-04-04T14:57:43ZRosich, AlbertOcampo-Martínez, CarlosModel predictive control (MPC) is one of the most used optimization-based control strategies for large-scale systems, since this strategy allows to consider a large number of states and multi-objective cost functions in a straightforward way. One of the main issues in the design of multi-objective MPC controllers, which is the tuning of the weights associated to each objective in the cost function, is treated in this work. All the possible combinations of weights within the cost function affect the optimal result in a given Pareto front. Furthermore, when the system has time-varying parameters, e.g., periodic disturbances, the appropriate weight tuning might also vary over time. Moreover, taking into account the computational burden and the selected sampling time in the MPC controller design, the computation time to find a suitable tuning is limited. In this regard, the development of strategies to perform a dynamical tuning in function of the system conditions potentially improves the closed-loop performance. In order to adapt in a dynamical way the weights in the MPC multi-objective cost function, an evolutionary-game approach is proposed. This approach allows to vary the prioritization weights in the proper direction taking as a reference a desired region within the Pareto front. The proper direction for the prioritization is computed by only using the current system values, i.e., the current optimal control action and the measurement of the current states, which establish the system cost function over a certain point in the Pareto front. Finally, some simulations of a multi-objective MPC for a real multi-variable case study show a comparison between the system performance obtained with static and dynamical tuning.