While classical control theory assumes that the controller has access to measurements of the entire state (or output) at every time instant, this paper investigates a setting where the feedback controller can only access a randomly selected subset of the state vector at each time step. Due to the random sparsification that selects only a subset of the state components at each step, we analyze the stability of the closed-loop system in terms of Asymptotic Mean-Square Stability (AMSS), which ensures that the system state converges to zero in the mean-square sense. We consider the problem of designing both a feedback gain matrix and a measurement sparsification strategy that minimizes the number of state components required for feedback, while ensuring AMSS of the closed-loop system. Interestingly, (1) we provide conditions on the dynamics of the system under which it is possible to find a sparsification strategy, and (2) we propose a Linear Matrix Inequality (LMI) based algorithm that jointly computes a stabilizing gain matrix, and a randomized sparsification strategy that minimizes the expected number of measured state coordinates while preserving the AMSS. Our approach is then extended to the case where the sparsification probabilities vary across the state components. Based on these theoretical findings, we propose an algorithmic procedure to compute the vector of sparsification parameters, along with the corresponding feedback gain matrix. To the best of our knowledge, this is the first study to investigate the stability properties of control systems that rely solely on randomly selected state measurements. Numerical simulations demonstrate that, in some settings, the system achieves comparable performance to full-state feedback while requiring measurements from only 0.3 percent of the state coordinates.

Reconfigurable Intelligent Surface (RIS) technology has gained significant attention due to its ability to enhance the performance of wireless communication systems. The main advantage of RIS is that it can be strategically placed in the environment to control wireless signals, enabling improvements in coverage, capacity, and energy efficiency. In this paper, we investigate a scenario in which a drone, equipped with a RIS, travels from an initial point to a target destination. In this scenario, the aerial RIS (ARIS) is deployed to establish a direct link between the base station and obstructed users. Our objective is to maximize the energy efficiency of the ARIS while taking into account its dynamic model including its velocity and acceleration along with the phase shift of the RIS. To this end, we formulate the energy efficiency problem under the constraints of the dynamic model of the drone. The studied problem is challenging to solve. To address this, we proceed as follows. First, we introduce an efficient solution that involves decoupling the phase shift optimization and the trajectory design. Specifically, the closed-form expression of the phase-shift is obtained using a convex approximation, which is subsequently integrated into the trajectory design problem. We then employ tools inspired by economic model predictive control (EMPC) to solve the resulting trajectory optimization. Our simulation results show a significant improvement in energy efficiency against the scenario where the dynamic model of the UAV is ignored.

We address a classical identification problem that consists in estimating a vector of constant unknown parameters from a given linear input/output relationship. The proposed method relies on a network of gradient-descent-based estimators, each of which exploits only a portion of the input-output data. A key feature of the method is that the input-output signals are hybrid, so they may evolve in continuous time (i.e., they may flow), or they may change at isolated time instances (i.e., they may jump). The estimators are interconnected over a weakly-connected directed graph, so the alternation of flows and jumps combined with the distributed character of the algorithm introduce a rich behavior that is impossible to obtain using continuous- or discrete-time estimators. A condition of persistence of excitation in hybrid form ensures exponential convergence of the estimation errors. The proposed approach generalizes the existing centralized gradient-descent algorithms and yields relaxed sufficient conditions for (uniform-exponential) parameter estimation. In addition, we address the observation/identification problem for a class of hybrid systems with unknown parameters using a distributed network of adaptive observers/identifiers.

In this work, we present an application of symbolic control and contract based design techniques to vehicle platooning. We use a compositional approach based on continuous-time assume-guarantee contracts. Each vehicle in the platoon is assigned an assume-guarantee contract; and a controller is synthesized using symbolic control to enforce the satisfaction of this contract. The assume-guarantee framework makes it possible to deal with different types of vehicles and asynchronous controllers (i.e controllers with different sampling periods). Numerical results illustrate the effectiveness of the approach.