Diagnostic en réseau de mobiles communicants, stratégies de répartition de diagnostic en fonction de contraintes de l'application

Abstract : In mobile robotics systems, the communication network is an important component of the overall system, it enables the system to accomplish its mission. Such a system is called Wireless Networked Control System WNCS where the integration of the wireless network into the control loop introduces problems that impact its performance and stability i.e, its quality of control (QoC). This QoC depends on the quality of service (QoS) therefore, the performance of the system depends on the parameters of the QoS. The study of the influence of wireless network defects on the QoC is crucial. WNCS is considered as a real-time system that requires a certain level of QoS for good performance. However, the probabilistic behavior of the CSMA / CA communication protocol used in most wireless technologies does not guarantee real-time constraints. A probabilistic method is then needed to analyze and define the application requirements in terms of QoS: delay, jitter, rate, packet loss. A first contribution of this thesis is to study the performance and reliability of an IEEE 802.11 wireless network for WNCSs that share the same network and the same control server by developing a stochastic model. This model is a Markov chain that models the access procedure to the communication channel. This model is used to define the QoS parameters that can guarantee the good QoC. In this thesis, we apply our approach to a mobile robot controlled by a remote station. The mobile robot aims to reach a target by avoiding obstacles, a classic example of mobile robotics applications. To ensure that its mission is accomplished, a probabilistic diagnostic method is essential because the system behavior is not deterministic. The second contribution of this thesis is to establish the probabilistic method used to monitor the robot mission and state. It is a modular Bayesian network BN that models cause-and-effect dependency relationships between failures that have an impact on the system QoC. The QoC degradation may be due either to a problem related to the internal state of the robot, a QoS problem or a controller problem. The results of the Markov model analysis are used in the modular BN to define its variables states (qualitative study) and to define the conditional probabilities of the QoS (quantitative study). It is an approach that permits to avoid the QoC degradation by making the right decision that ensures the continuity of the mission. In a co-design approach, when the BN detects a degradation of the QoC due to a bad QoS, the station sends an order to the robot to change its operation mode or to switch to another distant controller. Our hypothesis is that the diagnostic architecture depends on the operation mode. A distributed BN is used when the robot is connected to the station and a monolithic embedded BN when it is autonomous. Switching from a distributed controller to an on-board one involves updating the developed BN. Another contribution of this thesis consists in defining a switching strategy between the diagnostic modes: switching from a distributed BN to an on-board monolithic BN when the communication network takes no longer part of the system architecture and vice versa -versa. The inference and diagnostic scenarii results show the relevance of using distributed modular BNs. They also prove the ability of the developed BN to detect the degradation of QoC and QoS and to supervise the state of the robot. The modular structure of the BN facilitates the reconfiguration of the diagnostic policy according to the adapted control and communication architecture (distributed BN or on-board monolithic RB).
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Contributor : Abes Star <>
Submitted on : Tuesday, May 14, 2019 - 11:03:26 AM
Last modification on : Friday, May 17, 2019 - 11:42:12 AM


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Insaf Sassi. Diagnostic en réseau de mobiles communicants, stratégies de répartition de diagnostic en fonction de contraintes de l'application. Automatique / Robotique. Université Grenoble Alpes, 2017. Français. ⟨NNT : 2017GREAT121⟩. ⟨tel-02128323⟩



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