Distributed multi-agent Gaussian regression via finite-dimensional approximations

We consider the problem of distributedly estimating Gaussian processes in multi-agent frameworks. Each agent collects few measurements and aims to collaboratively reconstruct a common estimate based on all data. Agents are assumed with limited computational and communication capabilities and to gather $M$ noisy measurements in total on input locations independently drawn from a known common probability density. The optimal solution would require agents to exchange all the $M$ input locations and measurements and then invert an $M \times M$ matrix, a non-scalable task. Differently, we propose two suboptimal approaches using the first $E$ orthonormal eigenfunctions obtained from the \ac{KL} expansion of the chosen kernel, where typically $E \ll M$. The benefits are that the computation and communication complexities scale with $E$ and not with $M$, and computing the required statistics can be performed via standard average consensus algorithms. We obtain probabilistic non-asymptotic bounds that determine a priori the desired level of estimation accuracy, and new distributed strategies relying on Stein's unbiased risk estimate (SURE) paradigms for tuning the regularization parameters and applicable to generic basis functions (thus not necessarily kernel eigenfunctions) and that can again be implemented via average consensus. The proposed estimators and bounds are finally tested on both synthetic and real field data

Newton-Raphson Consensus for Distributed Convex Optimization

We address the problem of distributed uncon- strained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local private multidimensional convex cost, is subject to communication constraints, and wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proved, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton- Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.Comment: 18 pages, preprint with proof

Distributed MPC for Formation Path-Following of Multi-Vehicle Systems

The paper considers the problem of formation path-following of multiple vehicles and proposes a solution based on combining distributed model predictive control with parametrizations of the trajectories of the vehicles using polynomial splines. Introducing such parametrization leads indeed to two potential benefits: A) reducing the number of optimization variables, and b) enabling enforcing constraints on the vehicles in a computationally efficient way. Moreover, the proposed solution formulates the formation path-following problem as a distributed optimization problem that may then be solved using the alternating direction method of multipliers (ADMM). The paper then analyzes the effectiveness of the proposed method via numerical simulations with surface vehicles and differential drive robotspublishedVersio

Singularity-free Formation Path Following of Underactuated AUVs: Extended Version

This paper proposes a method for formation path following control of a fleet of underactuated autonomous underwater vehicles. The proposed method combines several hierarchic tasks in a null space-based behavioral algorithm to safely guide the vehicles. Compared to the existing literature, the algorithm includes both inter-vehicle and obstacle collision avoidance, and employs a scheme that keeps the vehicles within given operation limits. The algorithm is applied to a six degree-of-freedom model, using rotation matrices to describe the attitude to avoid singularities. Using the results of cascaded systems theory, we prove that the closed-loop system is uniformly semiglobally exponentially stable. We use numerical simulations to validate the results.Comment: Extended version of a paper, to appear in Proc. 2023 IFAC World Congress, 13 pages (9p + 4p appendices), 5 figure

Fast distributed estimation of empirical mass functions over anonymous networks

The aggregation and estimation of values over networks is fundamental for distributed applications, such as wireless sensor networks. Estimating the average, minimal and maximal values has already been extensively studied in the literature. In this paper, we focus on estimating empirical distributions of values in a network with anonymous agents. In particular, we compare two different estimation strategies in terms of their convergence speed, accuracy and communication costs. The first strategy is deterministic and based on the average consensus protocol, while the second strategy is probabilistic and based on the max consensus protocol

A Model Predictive Approach for Enhancing Transient Stability of Grid-Forming Converters

A model-based approach for controlling post-fault transient stability of grid-forming (GFM) converter energy resources is designed and analyzed. This proposed controller is activated when the converter enters into the saturated current operation mode. It aims at mitigating the issues arising from insufficient post-fault deceleration due to current saturation and thus improving the transient stability of the GFM Inverter Based Resources (IBRs). The considered approach conveniently modifies the post-fault trajectory of GFM IBRs by introducing appropriate corrective phase angle jumps and power references. These corrections are optimised following a model predictive approach (the model referring to post-fault dynamics of GFM IBRs in both saturated and normal operation modes). While constructing the proposed controller, the situation for GFM IBRs to enter into the saturated operation mode are identified. The effectiveness of this transient stability enhancement approach by means of dynamic simulations under various grid conditions is tested and discussed. The results demonstrate much better transient stability performance.Comment: 14 pages, 19 figure

Collision-avoiding model predictive rendezvous strategy to tumbling launcher stages

This paper considers the situation where a small satellite shall autonomously rendezvous with a tumbling object in a circular low Earth orbit (LEO) and derives a path-based model predictive controller that uses the docking point state and position of the chaser to guide it to a safe docking autonomously. The strategy embeds collision avoidance elements and reduces the computational effort for calculating the pulses to be provided by the thrusters through opportune algebraic manipulations, a Runge–Kutta 4 propagation method using linearized state transition matrices, and implicit embedding of dynamically equivalent thrust models, leading to constant state propagation matrices. Furthermore, the inputs design optimization problem and the embedded collision avoidance scheme are modeled and explicitly crafted as convex problems, contributing positively to low computational requirements. The docking and collision avoidance capabilities of the proposed scheme are extensively tested in an environment that accounts for all the perturbations relevant to LEO frameworks, for realistic thrust schemes, and for uncertainties in the measurement. Numerical results assess which tumbling objects can be docked or not by means of the proposed schemes as a function of the tumbling rates versus the thrust capabilities and hardware uncertainty of the docker