activity
20182026
most citedEnhancing the settling time estimation of a class of fixed-time stable systems

290 citations · 659 across the 17 of their papers we have counts for

collaborators

17 papers

eess.SY2026★ 23 cited

Autonomous and non-autonomous fixed-time leader-follower consensus for second-order multi-agent systems

Miguel A. Trujillo, Rodrigo Aldana-López, David Gomez Gutierrez +3

This paper addresses the problem of consensus tracking with fixed-time convergence, for leader-follower multi-agent systems with double-integrator dynamics, where only a subset of…

eess.SY2026

Super-twisting over networks: A Lyapunov approach for distributed differentiation

Rodrigo Aldana-López, Irene Perez Salesa, David Gomez Gutierrez +2

We study distributed differentiation, where agents in a networked system estimate the average of local time-varying signals and their derivatives under mild assumptions on the agen…

eess.SY2026

Hierarchical parameter estimation for distributed networked systems: a dynamic consensus approach

Ariana R. Mendez-Castillo, Rodrigo Aldana-Lopez, Antonio Ramirez-Trevino +2

This work introduces a novel two-stage distributed framework to globally estimate constant parameters in a networked system, separating shared information from local estimation. Th…

eess.SY2025★ 3 cited

Exact Leader Estimation: A New Approach for Distributed Differentiation

Rodrigo Aldana-Lopez, David Gomez-Gutierrez, Elio Usai +1

A novel strategy aimed at cooperatively differentiating a signal among multiple interacting agents is introduced, where none of the agents needs to know which agent is the leader,…

eess.SY2024★ 6 cited

Optimal robust exact first-order differentiators with Lipschitz continuous output

Rodrigo Aldana-Lopez, Richard Seeber, Hernan Haimovich +1

The signal differentiation problem involves the development of algorithms that allow to recover a signal's derivatives from noisy measurements. This paper develops a first-order di…

eess.SY2022

Implementing prescribed-time convergent control: sampling and robustness

Hernan Haimovich, Rodrigo Aldana-Lopez, Richard Seeber +1

According to recent results, convergence in a prespecified or prescribed finite time can be achieved under extreme model uncertainty if control is applied continuously over time. T…

eess.SY2022★ 19 cited

Dynamic consensus with prescribed convergence time for multi-leader formation tracking

Rodrigo Aldana-López, David Gómez-Gutiérrez, Rosario Aragüés +1

This work addresses the problem of distributed formation tracking for a group of follower holonomic mobile robots around a reference signal. The reference signal is comprised of th…

math.OC2021★ 18 cited

A predefined-time first-order exact differentiator based on time-varying gains

Aldana-López R., Gómez-Gutiérrez D., Trujillo M. A. +3

Recently, a first-order differentiator based on time-varying gains was introduced in the literature, in its non recursive form, for a class of differentiable signals , satisf…

eess.SY2020★ 21 cited

Predefined-Time Consensus of High-Order Multi-Agent Systems

J. Armando Colunga, Héctor M. Becerra, Carlos R. Vázquez +1

This paper addresses the distributed prescribed-time leader-following consensus problem for a class of high-order multi-agent systems (MASs) with perturbed nonlinear agents dynamic…

math.OC2019★ 35 cited

On predefined-time consensus protocols for dynamic networks

R. Aldana-López, D. Gómez-Gutiérrez, E. Jiménez-Rodríguez +2

This paper presents new classes of consensus protocols with fixed-time convergence, which enable the definition of an upper bound for consensus state as a parameter of the consensu…

cs.RO2019★ 46 cited

A Hybrid Method for Online Trajectory Planning of Mobile Robots in Cluttered Environments

Leobardo Campos-Macías, David Gómez-Gutiérrez, Rodrigo Aldana-López +2

This paper presents a method for online trajectory planning in known environments. The proposed algorithm is a fusion of sampling-based techniques and model-based optimization via…

cs.RO2019★ 45 cited

Autonomous Navigation of MAVs in Unknown Cluttered Environments

Leobardo Campos-Macías, Rodrigo Aldana-López, Rafael de la Guardia +2

This paper presents an autonomous navigation framework for reaching a goal in unknown 3D cluttered environments. The framework consists of three main components. First, a computati…

cs.CV2019

Real-time Approximate Bayesian Computation for Scene Understanding

Javier Felip, Nilesh Ahuja, David Gómez-Gutiérrez +2

Consider scene understanding problems such as predicting where a person is probably reaching, or inferring the pose of 3D objects from depth images, or inferring the probable stree…

math.OC2019★ 75 cited

On the design of new classes of fixed-time stable systems with predefined upper bound for the settling time

R. Aldana-López, D. Gómez-Gutiérrez, E. Jiménez-Rodríguez +2

This paper aims to provide a methodology for generating autonomous and non-autonomous systems with a fixed-time stable equilibrium point where an Upper Bound of the Settling Time (…

math.OC2018★ 48 cited

A class of robust consensus algorithms with predefined-time convergence under switching topologies

R. Aldana-López, D. Gómez-Gutiérrez, M. Defoort +2

This paper addresses the robust consensus problem under switching topologies. Contrary to existing methods, the proposed approach provides decentralized protocols that achieve cons…

eess.SY2018★ 30 cited

On finite-time and fixed-time consensus algorithms for dynamic networks switching among disconnected digraphs

David Gómez-Gutiérrez, Carlos Renato Vázquez, Sergej Čelikovský +2

The aim of this paper is to analyze a class of consensus algorithms with finite-time or fixed-time convergence for dynamic networks formed by agents with first-order dynamics. In p…

math.OC2018★ 290 cited

Enhancing the settling time estimation of a class of fixed-time stable systems

R. Aldana-López, D. Gómez-Gutiérrez, E. Jiménez-Rodríguez +2

This paper deals with the convergence time analysis of a class of fixed-time stable systems with the aim to provide a new non-conservative upper bound for its settling time. Our co…