MPC-based distributed formation control of multiple quadcopters with obstacle avoidance and connectivity maintenance

enero 9, 2023/en Investigadores, Publicaciones /por Hayet Jean Bernard

In this work, a distributed model predictive control (MPC) scheme based on consensus theory is proposed for the formation control of a group of quadcopters. The MPC scheme provides velocities for the quadcopters, which are considered as holonomic agents modeled at kinematic level. We propose soft and hard constraints for the MPC problem to address collision and obstacle avoidance as well as to maintain the connectivity of the communication topology during the motion of the agents to reach the desired formation. The contributions of this work are the following: First, we propose an integrated solution for the three tasks, including connectivity maintenance, which is uncommon in existing approaches, in addition to dynamic formation control and collision/obstacle avoidance. Second, we show that using both soft and hard constraints in the MPC problem gives better performance than using only one of the two. Third, unlike most MPC-based schemes from the literature, the effectiveness of our approach is validated through real experiments for a group of quadcopters.

Nonparametric Estimation of Functional Dynamic Factor Model

enero 9, 2023/en Investigadores, Publicaciones /por Israel A. Manzano Martínez, González Farías Graciela María de los Dolores

Data can be assumed to be continuous functions defined on an infinite-dimensional space for many phenomena. However, the infinite-dimensional data might be driven by a small number of latent variables. Hence, factor models are relevant for functional data. In this paper, we study functional factor models for time-dependent functional data. We propose nonparametric estimators under stationary and nonstationary processes. We obtain estimators that consider the time-dependence property. Specifically, we use the information contained in the covariances at different lags. We show that the proposed estimators are consistent. Through Monte Carlo simulations, we find that our methodology outperforms estimators based on functional principal components. We also apply our methodology to monthly yield curves. In general, the suitable integration of time-dependent information improves the estimation of the latent factors.