Overall objectives
Objectives
Control of interacting complex natural or artificial systems guaranteeing performance, safety and low computational burden is a challenge for the years to come.
Things have drastically changed from a material point of view in the last 15 years in engineering, biological and medical fields. For instance, in the field of robotics, the time when large robots, evolving in secure spaces where no human was supposed to be able to enter, could be managed with very basic control laws is over. Developing conntrol algorithms for robots of smaller size and having to adapt online their behaviour to the dynamics of the environment is much more difficult.
These topics are clearly the same in fields such as autonomous vehicles, unmanned aerial vehicles, traffic and energy networks etc. Moreover, in the case where several robots/vehicles/agents are cooperating, the question of communication is crucial and delays as well as disconnection are major issues in this context. The size of the network is certainly another key question.
Adopting a model-based approach, the aim of the team is to develop (optimal) control methods for (possibly large size) interconnected systems with the ultimate goal to produce implementable and low computational burden solutions (in the modest sense of favoring low complexity controllers from an implementation point of view).
We contribute to the modeling of chosen applications in Energy, bio-systems, Engineering and medicine. Partial Differential Equations (PDEs), Ordinary Differential Equations (ODEs) (possibly with delays), models will be considered in the linear as well as the nonlinear setting. Discretization of PDEs as well as the modeling of discrete measurements/actuation arising in continuous model systems will give rise to infinite or finite-dimensional discrete-time models. We also consider general classes of systems which encompass the framework of our applications such as Fractional Differential or Difference Equations.
Our goal is to analyze these models as much as possible without further simplifications in order to capture most of the phenomena (and, in particular, time-heterogeneity) and to develop control algorithms for them. This includes stability analysis, observation, robustness analysis and (optimal) control.
Note that we also perform some research at the confluence of Control Methods and Machine Learning (ML), not developing ourselves ML techniques to synthesize controllers from data but rather studying how System Theory and Optimization Theory can help analyzing closed-loop systems containing Neural Networks-based controllers.
