Exact Leader Estimation: A New Approach for Distributed Differentiation

TL;DR

This paper introduces a robust, exact distributed differentiation strategy that enables multiple agents to cooperatively estimate derivatives of a signal without knowing the leader, handling sampled-data communication and noise.

Contribution

It presents the first distributed leader-observer method capable of robust exact differentiation with sampled-data communication and bounded noise.

Findings

Effective in estimating derivatives up to arbitrary order m.

Achieves finite-time convergence under bounded higher derivatives.

Validated numerically for second- and fourth-order derivatives.

Abstract

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, i.e. the one producing the signal to be differentiated. Every agent communicates only a scalar variable to its neighbors; except for the leader, all agents execute the same algorithm. The proposed strategy can effectively obtain derivatives up to arbitrary $m$-th order in a finite time under the assumption that the $(m+1)$-th derivative is bounded. The strategy borrows some of its structure from the celebrated homogeneous robust exact differentiator by A. Levant, inheriting its exact differentiation capability and robustness to measurement noise. Hence, the proposed strategy can be said to perform robust exact distributed differentiation. In addition, and for the first time in the distributed leader-observer literature, sampled-data communication and bounded measurement noise are considered, and corresponding steady-state worst-case accuracy bounds are derived. The effectiveness of the proposed strategy is verified numerically for second- and fourth-order systems, i.e., for estimating derivatives of up to first and third order, respectively.