Showing papers by "Alessandro Rizzo published in 2023"
TL;DR: In this article , the authors study a population of individuals who play a generic $2$-action matrix game, and whose actions evolve according to a replicator equation, a nonlinear ordinary differential equation that captures salient features of the collective behavior of the population.
Abstract: Controlling evolutionary game-theoretic dynamics is a problem of paramount importance for the systems and control community, with several applications spanning from social sciences to ecological and engineering fields. Here, we study a population of individuals who play a generic $2$-action matrix game, and whose actions evolve according to a replicator equation -- a nonlinear ordinary differential equation that captures salient features of the collective behavior of the population. Our objective is to steer such a population to a specified equilibrium that represents a desired collective behavior -- e.g., to promote cooperation in the prisoner's dilemma. To this aim, we devise a class of adaptive-gain controllers, which regulate the system dynamics by adaptively changing a single entry of the payoff matrix of the game. The adaptive-gain controller is tailored according to distinctive features of the game, and conditions to guarantee global convergence to the desired equilibrium are established. Numerical simulations are provided to illustrate and corroborate our findings.
06 Jun 2023
TL;DR: In this paper , a hybrid auction-based task allocation architecture with multi-auctioneer agents' behavior is proposed for an urban air mobility application, which aims to solve the combined problem of: (i) scheduling parcel pick-up and delivery tasks with time deadlines while minimizing the drones' energy consumption; (ii) scheduling battery recharging tasks in order to ensure the service's persistency; and (iii) evaluating safe aerial routes since the UAVs fly over populated areas.
Abstract: Market-based task allocation methods represent an effective strategy for scheduling heterogeneous tasks to a heterogeneous multi-agent system, e.g., a fleet of different Unmanned Aerial Vehicles (UAVs). This is mainly due to their computational efficiency, ease of hybridization with optimization techniques and adaptability to different communication architectures. In this paper, a novel hybrid auction-based task allocation architecture with multi-auctioneer agents’ behavior is proposed for an Urban Air Mobility application. The proposed method aims to solve the combined problem of: (i) scheduling parcel pick-up and delivery tasks with time deadlines while minimizing the drones’ energy consumption; (ii) scheduling battery re-charge tasks in order to ensure the service’s persistency; and (iii) evaluating safe aerial routes since the UAVs fly over populated areas. The validity of the approach is demonstrated through Monte Carlo simulations. Moreover, being the proposed architecture distributed among the UAVs, the impact of communication failures on well-defined solution quality parameters is also investigated.
TL;DR: In this paper , the authors proposed a network epidemic model to elucidate the impact of deniers on the spread of epidemic diseases, and analyzed the epidemic threshold for large-scale homogeneous networks.
Abstract: We propose a novel network epidemic model to elucidate the impact of deniers on the spread of epidemic diseases. Specifically, we study the spread of a recurrent epidemic disease, whose progression is captured by a susceptible–infected–susceptible model, in a population partitioned into two groups: cautious individuals and deniers. Cautious individuals may adopt self-protective behaviors, possibly incentivized by information campaigns implemented by public authorities; on the contrary, deniers reject their adoption. Through a mean-field approach, we analytically derive the epidemic threshold for large-scale homogeneous networks, shedding light onto the role of deniers in shaping the course of an epidemic outbreak. Specifically, our analytical insight suggests that even a small minority of deniers may jeopardize the effort of public health authorities when the population is highly polarized. Numerical results extend our analytical findings to heterogeneous networks.
06 Jun 2023
TL;DR: In this article , a detailed nonlinear quadrotor model and a set of six mission scenarios are used to evaluate seven state-of-the-art linear and nonlinear controllers.
Abstract: A benchmark framework to test, evaluate, and compare different quadrotor controllers is presented. A detailed nonlinear quadrotor model and a set of six mission scenarios are used to evaluate seven state-of-the-art linear and nonlinear controllers. The quadrotor model is based on the Lagrange formulation and includes aerodynamic and gyroscopic effects, allows for sensor feedback noise to be introduced, and account for first order motor dynamics with input saturation. Simulated mission scenarios include realistic disturbances such as abrupt change of mass, wind gust, and aggressive flight maneuvers. The benchmark framework is the primary contribution of this research; the framework allows for performance comparison of multiple control architectures and implementations, and, the resulting open access testbed is made available to other researchers. Moreover, the same framework may be used to conduct simulated experiments (using ROS/Gazebo, X-Plane, or other software tools), and, with minor modifications, to compare controller performance based on real flights.