L'énergie est une ressource clé dans les réseaux de capteurs sans fil (WSNs), en particulier lorsque les nœuds capteurs sont alimentés par des batteries. Cette thèse s'inscrit dans le contexte de la réduction de la consommation de l'énergie d'un réseau de capteurs au niveau application construite au-dessus de ce réseau, grâce à des stratégies de contrôle, en temps réel et de façon dynamique. La première stratégie de gestion de l'énergie considérée s'appuie sur le contrôle prédictif (MPC). Le choix de MPC est motivé par les objectifs globaux qui sont de réduire la consommation d'énergie de l'ensemble des nœuds capteurs tout en assurant un service donné, nommé mission, pour le réseau de capteurs. En outre, un ensemble de contraintes sur les variables de contrôle binaires et sur les nœuds capteur doit être rempli. La deuxième stratégie de gestion de l'énergie au niveau de l'application utilise une approche de contrôle hybride (HDS). Ce choix est motivé par la nature inhérente du système WSN qui est par essence hybride, en particulier lorsque l'on s'intéresse à la gestion de l'énergie. La nature hybride vient essentiellement de la combinaison de processus physiques continus tels la charge et décharge des batteries des nœuds; tandis que la partie discrète est liée à la modification des modes de fonctionnement et l'état Inaccessible des nœuds. Les stratégies proposées sont évaluées et comparées en simulation sur des différents scenarios réalistes. Elles ont aussi \'et\'e mises en œuvre sur un banc d'essai réel et les résultats obtenus ont été discutés. / Energy is a key resource in Wireless Sensor Networks (WSNs), especially when sensor nodes are powered by batteries. This thesis is investigates how to save energy of the whole WSN, at the application level, thanks to control strategies, in real time and in a dynamic way. The first energy management strategy investigated is based on Model Predictive Control (MPC). The choice of MPC is motivated by the global objectives that are to reduce the energy consumption of the set of sensor nodes while ensuring a given service, named mission, for the sensor network. Moreover, a set of constraints on the binary control variables and on the sensor modes must be fulfilled. The second energy management strategy at the application level is based on a Hybrid Dynamical System (HDS) approach. This choice is motivated by the hybrid inherent nature of the WSN system when energy management is considered. The hybrid nature basically comes from the combination of continuous physical processes, namely, the charge / discharge of the node batteries; while the discrete part is related to the change in the functioning modes and the Unreachable condition of the nodes. The proposed strategies are evaluated and compared in simulation on a realistic test-case. Lastly, they have been implemented on a real test-bench and the results obtained have been discussed.

There is an increasing interest in automated mobile equipment in the construction, agriculture and mining industries to improve productivity, efficiency and operator safety. In general, these machines belong to a class of mobile vehicles with a tool for manipulating its environment to accomplish a repetitive task. Forces and motions are inherently coupled between the tool (<em>e.g.</em> bucket or blade) and the means of vehicle propulsion (<em>e.g</em>. wheels or tracks). Furthermore, they are often operated within uncertain and unstructured environments. A particularly challenging case involves the use of a bulldozer for the removal of excavated material. Modeling and control of mobile robots that interact forcibly with their environment, such as robotic excavation machinery, is a challenging problem that has not been adequately addressed in prior research. This thesis investigates the low-level modeling and control of a 3-DOF robotic bulldozing operation. Motivated by a bulldozing process in an underground mining application, a theoretical nonlinear hybrid dynamic model was developed. The model includes discrete operation modes contained within a hybrid dynamic model framework. The dynamics of the individual modes are represented by a set of linear and nonlinear differential equations. An instrumented scaled-down bulldozer and environment were developed to emulate the full scale operation. Model parameter estimation and validation was completed using experimental data from this system. The model was refined based on a global sensitivity analysis. The refined model was found to be suitable for simulation and design of robotic bulldozing control laws. Optimal blade position control laws were designed based on the hybrid dynamic model to maximize the predicted material removal rate of the bulldozing process. A stability analysis of the underlying deterministic closed-loop process dynamics was performed using Lyapunov’s second method. Monte Carlo simulation was used for further performance and stability analysis of the closed-loop process dynamics including stochastic state disturbances and input constraints. Results of the Monte Carlo simulation were also used for tuning the blade position control laws. Experiments were conducted with the scaled-down robotic bulldozing system. The control laws were implemented with various tuning values. As a comparison, a rule-based blade control algorithm was also designed and implemented. The experimental results with the optimal control laws demonstrated a 33% increase in the average material removal rate compared to the rule-based controller. / Doctor of Philosophy (PhD)