This paper is concerned with a special case of stochastic distributed optimal control, where the objective is to design a structurally constrained controller for a system subject to state and input power constraints. The structural constraints are induced by the directed communication between local controllers over a strongly connected graph. Based on the information structure present, that is, who knows what and when, we provide a control synthesis with the optimal control law consisting of two parts: one that is based on the common information between the subsystems and one that uses more localized information. The developed method is applicable to an arbitrary number of physically interconnected subsystems.

In this paper we address the problem of optimal co-design of control and quantization policies for a physically-interconnected system, where each subsystem has a local quantizer. The controllers are assumed to communicate with delay and cooperate in minimizing global quadratic cost. We show that for quantizers that act on the estimation error of the estimator conditioned on common information between controllers, separation holds. In other words, both quantizers can be optimally designed by minimizing a distortion function that is control-independent. Finally, for general class of quantizers we provide structural properties of the optimal control policy.

In this paper we consider a finite-horizon optimization problem with a distributed control policy. The local outputs are sent to a local controller in an intermittent fashion. As a consequence the controller has access to sensor information only if it is sent by the associated local scheduler or by neighboring controllers. We consider generalized event-triggered schedulers (which includes time-triggered schedulers as a special case, where time-instants define the events). This leads to an event-dependent information structure available at each local controller. As a result, the information structure changes, which potentially leads to a non-convex control design problem. For any event-triggered sensing topology, we give a necessary and sufficient condition for convexity of the optimal control problem, by using the quadratic invariance (QI) property. Furthermore, we provide an online algorithm that adapts the communication topology among the local controllers and guarantees a step-by-step QI, which translates to a global QI.

Controlling physical machinery and processes is at the core of production automation. However, challenged by inflexibility, automation and control is evaluating to outsource this control to resourceful cloud environments. While this enables to derive better control through a plethora of measurements, it challenges the control quality through delay introduced through networks. In this paper, we show how to unify control and communication by offloading delay sensitive control tasks from the cloud to local network elements --- a previously unexplored area for in-network processing --- enabling both, ultra-high quality-of-control and scalable orchestration through cloud environments. Our implementation demonstrates how we combine state of the art control with communication. We achieve this by expressing the control and the datapath in P4 which we synthesize to BPF programs that we execute in XDP environments on Netronome SmartNICs. Further, we highlight the demands of control towards communication to build more involved and complex in-network controllers.

We address a cooling energy management problem in a multi-building setting where buildings need to maintain comfort conditions for the occupants by keeping their zones temperature within a certain range. To this purpose, each one of them has its own chiller and is connected to a shared cooling network. The goal is to minimize the overall district electricity cost over some finite time horizon by optimally setting the temperature set-points in the buildings and the energy exchange with the cooling network, compatibly with comfort and actuation constraints, while accounting for uncertainty, mainly due to outside temperature, people occupancy, and solar radiation. To this purpose, a distributed version of the scenario approach to stochastic constrained optimization is adopted, which allows to guarantee by design a predefined robustness level of the obtained solution against uncertainty.

In this paper we address the problem of information-constrained optimal control for an interconnected system subject to one-step communication delays and power constraints. The goal is to minimize a finite-horizon quadratic cost by optimally choosing the control inputs for the subsystems, accounting for power constraints in the overall system and different informationavailable at the decision makers. To this purpose, due to the quadratic nature of the power constraints, the LQG problem is reformulated as a linear problem in the covariance of state-input aggregated vector. The zero- duality gap allows us to equivalently consider the dual problem,and decompose it into several sub-problems according to the information structure present inthe system. Finally, the optimal control inputs are found in a form that allows for offline computation of the control gains.