Ricerc@Sapienza

The deep integration between the cyber and physical domains in complex systems make very challenging the security evaluation process, as security itself is more of a concept (i.e. a subjective property) than a quantifiable characteristic. Traditional security assessing mostly relies on the personal skills of security experts, often based on best practices and personal experience.

The ever-increasing deployment of autonomous Cyber-Physical Systems (CPSs) (e.g., autonomous cars, UAV) exacerbates the need for efficient formal verification methods. In this setting, the main obstacle to overcome is the huge number of scenarios to be evaluated. Statistical Model Checking (SMC) is a simulation-based approach that holds the promise to overcome such an obstacle by using statistical methods in order to sample the set of scenarios. Many SMC tools exist, and they have been reviewed in several works.

This article deals with the distributed infinite-horizon
linear-quadratic-Gaussian optimal control problem for continuous-time systems
over networks. In particular, the feedback controller is composed of local
control stations, which receive some measurement data from the plant process
and regulates a portion of the input signal. We provide a solution when the
nodes have information on the structural data of the whole network but takes
local actions, and also when only local information on the network are available

This paper delas with the distributed infinite-horizon Linear Quadratic Gaussian optimal control probelm for continuous-time systems over networks.