38 research outputs found
A Review of Shared Control for Automated Vehicles: Theory and Applications
The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years.This work was supported in part by the ECSEL Joint Undertaking, which funded the PRYSTINE project under Grant 783190, and in part by the AUTOLIB project (ELKARTEK 2019 ref. KK-2019/00035; Gobierno Vasco Dpto. Desarrollo económico e infraestructuras)
Review of neural modelling on cardiovascular rehabilitation active processes by using cycloergometers
Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58This work gathers important developments carried out in a specific
area of the Biomedical Engineering which applies advanced models based on
Artificial Neural Networks to improve Cardiovascular Rehabilitation (CR) processes
by using Cycloergometers. This work presents an updated revision of proposals,
focusing on different problems involved in CR and considering features
and requirements nowadays taken into account during their modelling processes.
Furthermore, the signals analysed in these models are studied and presented below.
Among them, a review of solutions applied to CR processes, focused on
Computational Intelligence are cited.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona
Review of neural modelling on cardiovascular rehabilitation active processes by using cycloergometers
Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58This work gathers important developments carried out in a specific
area of the Biomedical Engineering which applies advanced models based on
Artificial Neural Networks to improve Cardiovascular Rehabilitation (CR) processes
by using Cycloergometers. This work presents an updated revision of proposals,
focusing on different problems involved in CR and considering features
and requirements nowadays taken into account during their modelling processes.
Furthermore, the signals analysed in these models are studied and presented below.
Among them, a review of solutions applied to CR processes, focused on
Computational Intelligence are cited.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona
Hydraulic Press Commissioning Cost Reductions via Machine Learning Solutions
Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58In industrial processes, PI controllers remain as the dominant control technique due to their applicability and performance reliability. However, there could be applications where the PI controller is not enough to fulfill certain specifications, such as in the force control loop of hydraulic presses, in which specific pressure profiles need to be ensured in order not to damage theworkpiece. An Iterative Learning Control scheme is presented as a Machine Learning control alternative to the PI controller, in order to track the pressure profiles required for any operational case. Iterative Learning Control is based on the notion that a system that realizes the same process repeatedly, e.g. hydraulic presses, can improve its performance by learning from previous iterations. The improvements are revealed in high-fidelity simulations of a hydraulic press model, in which the tracking performance of the PI controller is considerably improved in terms of
overshoot and the settling time of pressure signal.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona
Hydraulic Press Commissioning Cost Reductions via Machine Learning Solutions
Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58In industrial processes, PI controllers remain as the dominant control technique due to their applicability and performance reliability. However, there could be applications where the PI controller is not enough to fulfill certain specifications, such as in the force control loop of hydraulic presses, in which specific pressure profiles need to be ensured in order not to damage theworkpiece. An Iterative Learning Control scheme is presented as a Machine Learning control alternative to the PI controller, in order to track the pressure profiles required for any operational case. Iterative Learning Control is based on the notion that a system that realizes the same process repeatedly, e.g. hydraulic presses, can improve its performance by learning from previous iterations. The improvements are revealed in high-fidelity simulations of a hydraulic press model, in which the tracking performance of the PI controller is considerably improved in terms of
overshoot and the settling time of pressure signal.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona
Uso de redes neuro-borrosas RFNN para la aproximación del comportamiento de una neuroprótesis de antebrazo en pacientes con daño cerebral
Las neuroprótesis son sistemas basados en la técnica de estimulación eléctrica funcional que provocan contracciones musculares mediante la excitación artificial de nervios periféricos, y son utilizadas para sustituir funciones motrices/sensoriales en aplicaciones tanto asistivas como terapéuticas. Este trabajo presenta la posibilidad de utilizar redes neuro-borrosas recurrentes para obtener modelos capaces de extraer las características principales del resultado de la aplicación de una neuroprótesis de miembro superior en distintos pacientes. Se ha entrenado una Recurrent Fuzzy Neural Network (RFNN) con datos reales obtenidos de pacientes crónicos de daño cerebral adquirido. Se han analizado distintas estrategias y estructuras y los resultados preliminares muestran la
capacidad de estas redes de aprender las características principales de distintos sujetos y de proporcionar información fácilmente interpretable
A Review of Shared Control for Automated Vehicles: Theory and Applications
The last decade has shown an increasing interest on advanced driver assistance systems (ADAS) based on shared control, where automation is continuously supporting the driver at the control level with an adaptive authority. A first look at the literature offers two main research directions: 1) an ongoing effort to advance the theoretical comprehension of shared control, and 2) a diversity of automotive system applications with an increasing number of works in recent years. Yet, a global synthesis on these efforts is not available. To this end, this article covers the complete field of shared control in automated vehicles with an emphasis on these aspects: 1) concept, 2) categories, 3) algorithms, and 4) status of technology. Articles from the literature are classified in theory- and application-oriented contributions. From these, a clear distinction is found between coupled and uncoupled shared control. Also, model-based and model-free algorithms from these two categories are evaluated separately with a focus on systems using the steering wheel as the control interface. Model-based controllers tested by at least one real driver are tabulated to evaluate the performance of such systems. Results show that the inclusion of a driver model helps to reduce the conflicts at the steering. Also, variables such as driver state, driver effort, and safety indicators have a high impact on the calculation of the authority. Concerning the evaluation, driver-in-the-loop simulators are the most common platforms, with few works performed in real vehicles. Implementation in experimental vehicles is expected in the upcoming years
From the Concept of Being “the Boss” to the Idea of Being “a Team”: The Adaptive Co-Pilot as the Enabler for a New Cooperative Framework
The “classical” SAE LoA for automated driving can present several drawbacks, and
the SAE-L2 and SAE-L3, in particular, can lead to the so-called “irony of automation”, where the
driver is substituted by the artificial system, but is still regarded as a “supervisor” or as a “fallback
mechanism”. To overcome this problem, while taking advantage of the latest technology, we regard
both human and machine as members of a unique team that share the driving task. Depending on
the available resources (in terms of driver’s status, system state, and environment conditions) and
considering that they are very dynamic, an adaptive assignment of authority for each member of the
team is needed. This is achieved by designing a technology enabler, constituted by the intelligent
and adaptive co-pilot. It comprises (1) a lateral shared controller based on NMPC, which applies
the authority, (2) an arbitration module based on FIS, which calculates the authority, and (3) a visual
HMI, as an enabler of trust in automation decisions and actions. The benefits of such a system are
shown in this paper through a comparison of the shared control driving mode, with manual driving
(as a baseline) and lane-keeping and lane-centering (as two commercial ADAS). Tests are performed
in a use case where support for a distracted driver is given. Quantitative and qualitative results
confirm the hypothesis that shared control offers the best balance between performance, safety, and
comfort during the driving task.This research was supported by the ECSEL Joint-Undertaking,which funded the PRYSTINE
project under the Grant 783190
Implementación de un sistema de control predictivo inteligente para sistemas de dinámicas complejas
[ES] Este trabajo presenta la metodología que se está empleando, dentro del grupo de investigación de control inteligente (GICI) de la UPV/EHU, para el desarrollo de estrategias de control inteligente y su posterior implementación en plataformas de tiempo real. De este modo se pretende realizar una validación de dichas estrategias no solamente desde el punto de vista de simulación, sino acercando estos desarrollos a diferente hardware industrial. El caso de uso que se presenta y que está siendo implementado actualmente es el de la estrategia iMO-NMPC, el cual integra dentro de una estrategia de control predictivo, algoritmos evolutivos para la optimización y redes neuronales para el modelado de sistemas. La metodología que se está empleando hace uso de la plataforma de simulación MATLAB/Simulink®.[EN] This work presents the methodology used by the Intelligent Control Research Group (GICI) at UPV/EHU, for the development of intelligent control strategies and their further implementation in real time platforms. In this way, it is intended to provide validation of such strategies not only in simulation level but in several industrial devices. The use case that is being developed is the iMO-NMPC strategy which integrates predictive control strategies, evolutionary algorithms for optimization and neural networks
for system modelling. The employed methodology involves the simulation platform MATLAB/Simulink ®
Estudio de estructuras neuronales NARX para reproducir el comportamiento de sistemas con dinámicas complejas
[ES] Este trabajo presenta un estudio preliminar donde se valorar´a la eficiencia de las redes neuronales artificiales de topología NARX (Nonlinear Autoregressive eXogenous) en la reproducción del comportamiento de sistemas con dinámicas complejas. Estas estructuras neuronales se diseñarán para reproducir tanto sistemas monovariables, como multivariables, siguiendo un mismo planteamiento metodológico. Los mencionados estudios están dirigidos a proporcionar dichos modelos neuronales a futuras estrategias de control dependientes de modelos dinámicos, como es el caso del control predictivo no lineal basado en modelos, el cual constituye una línea de trabajo dentro del grupo de investigación de control inteligente (GICI) de la UPV/EHU.[EN] This work presents a preliminary study that evaluates the NARX artificial neural network performance in reproducing the the behaviour of complex dynamics systems. These neural structures will be designed to reproduce both monovariable and multivariable systems, following the same methodological approach. These studies are aimed at providing neural models to future control strategies dependent on dynamic models, as is the case of non-linear predictive control based on models, which constitutes a new line of work within the Intelligent Control Research Group (GICI) at UPV/EH