6 citations · 15 across the 4 of their papers we have counts for
7 papers
Abstract homogeneous chains: a Lyapunov framework for high-order sliding modes in multi-agent systems
Rodrigo Aldana-López
This work develops a Lyapunov framework for a broad class of arbitrary-order sliding-mode algorithms in multi-agent systems. We introduce abstract homogeneous chains, a class of no…
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…
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,…
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…
Differentiator for Noisy Sampled Signals with Best Worst-Case Accuracy
Hernan Haimovich, Richard Seeber, Rodrigo Aldana-López +1
This paper proposes a differentiator for sampled signals with bounded noise and bounded second derivative. It is based on a linear program derived from the available sample informa…
Designing predefined-time differentiators with bounded time-varying gains
Rodrigo Aldana-López, Richard Seeber, David Gómez-Gutiérrez +2
There is an increasing interest in designing differentiators, which converge exactly before a prespecified time regardless of the initial conditions, i.e., which are fixed-time con…