Juan S. Mejía

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We study the problem of formation control and trajectory tracking for a group of robotic systemsmodeled by Lagrangian dynamics. The objective is to achieve and maintain a stable formation for a group of multiagent systems, while guaranteeing tracking of a specified trajectory. In order to do so, we partition the state space for the collective system into(More)
The formulation and solution of a minimum time optimal control problem for a formation conformed by nonholonomic car-like mobile robots and a virtual leader reaching a target zone in an environment that includes dynamic and static obstacles with arbitrary shapes, is provided in this paper. The proposed approach for solving the formation to target zone(More)
In this paper we revisit the contractive model predictive control framework and propose a new contractive constraint, which depending on selected candidate Lyapunov function and contracting factor can guarantee different types of system's stability. Simulation results are presented to illustrate the effect of the proposed contractive scheme.
In this paper we propose a decentralized control policy for coordinating a group of nonholonomic autonomous vehicles (non necessarily homogeneous) with input constraints moving in ¿<sup>2</sup>, aiming to satisfy individual (trajectory tracking) and common objectives (guaranteed vehicles safety). The proposed policy models the discrete operation modes of(More)
This paper presents a new constructive model predictive control approach to asymptotic stabilization of constrained, discrete time-invariant nonlinear dynamic systems. The constructive approach not only considers the traditional optimality problem on a finite horizon, but also considers a feasibility constraint imposed at the end of each finite horizon(More)
This article proposes a method for controlling formations of autonomous nonholonomic vehicles in order to reach a desired target region. The approach is based on utilization of pairs of virtual leaders whose control inputs are obtained in a single optimization process using Model Predictive Control (MPC) methodology. The obtained solution of the(More)
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