In the present dissertation, a team-based coverage control is proposed that aims at deploying groups of agents to an environment with a probability distribution function representing the likelihood of an event in different regions. The proposed approach can handle the deployment of heterogeneous teams agents each of which pursing a different objective or assigned task. The presented approaches are then implemented on a set of numerical examples to asses their performance. As the next task and in a different domain, the development of robust nonlinear control techniques is studied for uncertain systems. Various types of uncertainty is investigated in three major areas; first, a robust identification approach is proposed for the emph{Linear Parameter Varying} (LPV) identification of nonlinear systems with uncertain Scheduling variables. Two deterministic and stochastic techniques are developed and their performance are compared with the previous methods in the literature. Next, a robust reduced-order model based controller is designed for a system represented by a parabolic emph{Partial Differential Equation} (PDE). The objective is to take into account the variation of the model parameter and its effect on the reduced-order model. Then, the reduced model is used to design a robust nonlinear controller to control the main full-order model.