Contrôle collaboratif entre un pilote humain et un module semi-autonome de navigation

RÉSUMÉ L'intervention d'un pilote humain dans le processus de contrôle d'une plate-forme mobile par un module semi-autonome de navigation pose le problème complexe de partage de contrôle. Ce problème est d'autant plus difficile à résoudre lorsque le module semi-autonome et le pilote ne partagent pas les mêmes ensembles d'observations et ne réagissent pas de la même manière devant un contexte de dangers. Le but de cette thèse est de développer une approche de contrôle collaboratif entre un pilote humain et un module semi-autonome de navigation ayant chacun différents systèmes de perception de danger et différentes manières d'action face au danger. En considérant que le module semi-autonome assiste le pilote dans ses manoeuvres de navigation, nous proposons une approche de contrôle collaboratif basée sur l'estimation de l'aptitude du pilote et comportant deux modules principaux: un module semi-autonome intégrant directement les signaux de contrôle du pilote et un module de délibération basé sur l'estimation de l'entropie comportementale du pilote. Le module semi-autonome utilise une version modifiée de l'approche d'évitement de dangers basée sur la méthode des champs de potentiels artificiels. La modification introduite vise à réduire les effets négatifs de la version classique sur le système de navigation collaboratif. En effet, la méthode classique d'évitement de dangers basée sur l'approche des champs de potentiel artificiel produit souvent des mouvements oscillatoires lorsque l'espace de navigation est contraint. Par ailleurs, cette méthode peut engendrer des directions de déplacements éloignées de celles désirées par le pilote. Ce qui n'est pas souhaitable. La méthode de champ de potentiel directionnel présentée dans cette thèse consiste à pondérer la force artificielle répulsive d'un danger en fonction de sa position géométrique par rapport à la direction du mouvement de la plate-forme avant de l'intégrer dans le calcul de la force répulsive artificielle résultante de tous les dangers immédiats. Comme la direction du mouvement provient des observations des signaux de contrôle du pilote, cette méthode privilégie essentiellement les dangers qui entravent directement le mouvement de la plate-forme. --------- ABSTRACT The intervention of a human agent when a semi-autonomous navigation module is driving a mobile platform raises the shared control problem. This problem is especially complex when the semi-autonomous module and the human agent do not have the same perception system and do not avoid imminent dangers in the same way. The aim of this thesis is to develop a collaborative control approach between a human agent and a semi-autonomous navigation module by considering that the agents have different perception systems and different ways to avoid dangers. By considering that the semi-autonomous navigation module helps the human agent during its navigation maneuvers, we propose a collaborative control approach based on an estimation of the human agent ability. The system consists of two main modules: a semi-autonomous navigation module which allows the direct integration of the human agent control signals and a deliberative module based on the estimation of the behavioral entropy of the human agent. The semi-autonomous navigation module uses a modified version of the well-known artificial potential field danger avoidance approach. This modification aims at reducing the negative effects of the classical version on the collaborative navigation system. Indeed, the classical method often produces oscillations when the navigation space is cluttered. Moreover, it may produce motion directions far from those desired by the human agent. The method of directional potential field proposed in this thesis consists in weighting the artificial repulsive force of a danger based on its geometrical position relative to the platform motion direction. As this motion direction is derived from the sequence of the human agent control signals, the directional potential field method focuses mainly on dangers that directly interfere with the platform movement. However, the use of the proposed danger avoidance method increases the occurrence of the local minimum problem. The presence of a local minimum leads the platform to stop, despite the desire of movement of the human agent. We have therefore, introduced the concept of the mechanical reflex arc defined as the association between a danger context and the human agent control signals

Investigating the impact of ageing and thermal management of a fuel cell system on energy management strategies

This paper studies the impact of two significant aspects, namely fuel cell (FC) degradation and thermal management, over the performance of an optimal and a rule-based energy management strategy (EMS) in a fuel cell hybrid electric vehicle (FCHEV). To do so, firstly, a vehicle's model is developed in simulation environment for a low-speed FCHEV composed of a FC stack and a battery pack. Subsequently, deterministic dynamic programming (DP), as an optimal strategy, and bounded load following strategy (BLFS), as a common rule-based strategy, are utilized to minimize the hydrogen consumption while respecting the operating constraints of the power sources. The performance of the EMSs is assessed at different scenarios. The first objective is to clarify the effect of FC stack degradation on the performance of the vehicle. In this regard, each EMS determines the required current from the FC stack for two FCs with different levels of degradation. The second objective is to evaluate the thermal management contribution to improving the performance of the new FC compared to the considered cases in scenario one. In this respect, each strategy deals with determining two control variables (FC current and cooling fan duty cycle). The results of this study indicate that negligence of adapting to the PEMFC health state, as the PEMFC gets aged, can increase the hydrogen consumption up to 24.8% in DP and 12.1% in BLFS. Moreover, the integration of temperature dimension into the EMS can diminish the hydrogen consumption by 4.1% and 5.3% in DP and BLFS respectively. © 2020 Elsevier Lt

Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells

Cold start of proton exchange membrane fuel cells (PEMFCs) at sub-zero temperatures is perceived as one of the obstacles in their commercialization way in automotive application. This paper proposes a novel internal-based adaptive strategy for the cold start of PEMFC to control its operating current in real time in a way to maximize the generated heat flux and electrical power in a short time span. In this respect, firstly, an online parameter identification method is integrated into a semi-empirical model to cope with the PEMFC performances drifts during cold start. Subsequently, an optimization algorithm is launched to find the best operating points from the updated model. Finally, the determined operating point, which is the current corresponding to the maximum power, is applied to PEMFC to achieve a rapid cold start. It should be noted that the utilization of adaptive filters has escaped the attention of previous PEMFC cold start studies. The ultimate results of the proposed strategy are experimentally validated and compared to the most commonly used cold start strategies based on Potentiostatic and Galvanostatic modes. The experimental outcomes of the comparative study indicate the striking superior performance of the proposed strategy in terms of heating time and energy requirement. © 2018 Elsevier Lt

Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications

The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature

Efficiency upgrade of hybrid fuel cell vehicles' energy management strategies by online systemic management of fuel cell

This paper puts forward an approach for boosting the efficiency of energy management strategies (EMSs) in fuel cell hybrid electric vehicles (FCHEVs) using an online systemic management of the fuel cell system (FCS). Unlike other similar works which solely determine the requested current from the FCS, this work capitalizes on simultaneous regulation of current and temperature, which have different dynamic behavior. In this regard, firstly, an online systemic management scheme is developed to guarantee the supply of the requested power from the stack with the highest efficiency. This scheme is based on an updatable 3D map which relates the requested power from the stack to its optimal temperature and current. Secondly, two different EMSs are used to distribute the power between the FCS and battery. The EMSs' constraints are constantly updated by the online model to embrace the stack performance drifts owing to degradation and operating conditions variation. Finally, the effect of integrating the developed online systemic management into the EMSs' design is experimentally scrutinized under two standard driving cycles and indicated that up to 3.7% efficiency enhancement can be reached by employing such a systemic approach. Moreover, FCS health adaptation unawareness can increase the hydrogen consumption up to 6.6%

A comprehensive review of solutions and strategies for cold start of automotive proton exchange membrane fuel cells

Proton exchange membrane fuel cell (PEMFC) can be a significant eco-friendly alternative power source for vehicles. However, under subfreezing conditions, cell degradation and irreversible performance decay can occur because of ice formation and repetitive thaw/freeze cycles. These problems have limited the further commercialization of PEMFC in cold weather countries. Thus, many improvements have been made to repair the freeze protection and rapid cold startup problems in PEMFC vehicles. In this paper, a comprehensive review dedicated to engineers of the recent research progress on the PEMFC cold start problems is presented. Systems and methods for fuel cell shutdown are summarized and classified into two categories: purge solution and material to avoid freezing. Regarding the system and solutions for PEMFC cold startup, different heating solutions are classified into two main groups depending on their heating sources and categorized as internal and external heating methods. This paper concludes with a detailed review of cold startup strategies based on an exhaustive survey of journal papers and patents. © 2016 IEEE

Comparative analysis of two online identification algorithms in a fuel cell system

Output power of a fuel cell (FC) stack can be controlled through operating parameters (current, temperature, etc.) and is impacted by ageing and degradation. However, designing a complete FC model which includes the whole physical phenomena is very difficult owing to its multivariate nature. Hence, online identification of a FC model, which serves as a basis for global energy management of a fuel cell vehicle (FCV), is considerably important. In this paper, two well-known recursive algorithms are compared for online estimation of a multi-input semi-empirical FC model parameters. In this respect, firstly, a semi-empirical FC model is selected to reach a satisfactory compromise between computational time and physical meaning. Subsequently, the algorithms are explained and implemented to identify the parameters of the model. Finally, experimental results achieved by the algorithms are discussed and their robustness is investigated. The ultimate results of this experimental study indicate that the employed algorithms are highly applicable in coping with the problem of FC output power alteration, due to the uncertainties caused by degradation and operation condition variations, and these results can be utilized for designing a global energy management strategy in a FCV. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Stable and Adaptive Control for Wheeled Mobile Platform

ABSTRACT: Most differential drive platforms are equipped with two independent actuators and casters. The positions of the gravity center and the rotation center often do not coincide. This position difference, combined with the effect of unbalanced actuator dynamics on the motion, makes it difficult to properly control the platform. We propose an adaptive nonlinear controller system based on the Lyapunov stability theory that greatly improves the trajectory tracking performance of such platforms. The asymptotically stable kinematic controller takes into account the position difference and the effect of the unbalanced actuator dynamics. The dynamic controller has the desirable property that it requires minimal knowledge of the platform physical parameters. Validation was performed through simulation and several experiments conducted on a rear driven powered wheelchair. Comparative experimental studies suggested that the proposed adaptive control system performs better than a similar method presented in the literature for linear as well as curvilinear trajectory tracking. Furthermore, the control system exhibits good tracking performance on inclined plans and non smooth surfaces