Decentralized Stochastic MPC

In several applications, such as area surveillance, the task is so complex that the need to decompose the problem into multiple subsystems (e.g. cameras in an indoor scenario) which coordinate their actions arises. Moreover, the operating conditions may vary randomly (moving targets, external disturbances, etc.), and relying on a centralized unit that computes the overall system actions may be computationally costly and critical in case of failures. In order to alleviate such problems, we adopt a Decentralized Stochastic MPC approach in which the complex global problem is divided into relatively simpler local problems that can be handled by each subsystem. However, this distribution of subtasks, if done arbitrarily, can compromise the ability to perform the required task and may lead to performance deterioration if not instability. Some crucial questions that naturally arise are the following:

• Is the decentralized scheme stable?
• How well does it perform with respect to some global performance index for the whole system?
• What kind of information exchange is to be carried out between the subsystems?
• In case the operational conditions vary, how do the subsystems exchange subtasks among them so that the overall performance is not compromised?

We investigate these questions motivated by applications in the areas of air traffic management and surveillance camera networks.