PI Consensus Error Transformation for Adaptive Cooperative Control of Nonlinear Multi-Agent Systems

TL;DR

This paper introduces a novel PI consensus error transformation for adaptive control of nonlinear multi-agent systems with unknown control directions, simplifying the design of distributed controllers to achieve asymptotic consensus.

Contribution

It proposes a new variable transformation that decouples control design, enabling effective adaptive consensus control in complex multi-agent systems with unknown, non-identical control directions.

Findings

The PI consensus error transformation ensures boundedness and convergence to zero.

Distributed controllers based on Nussbaum gain techniques achieve consensus.

Simulation results confirm theoretical predictions.

Abstract

A solution is provided in this note for the adaptive consensus problem of nonlinear multi-agent systems with unknown and non-identical control directions assuming a strongly connected underlying graph topology. This is achieved with the introduction of a novel variable transformation called PI consensus error transformation. %that allows for decoupled consensus control design of multi-agent systems. The new variables include the position error of each agent from some arbitrary fixed point along with an integral term of the weighted total displacement of the agent's position from all neighbor positions. It is proven that if these new variables are bounded and regulated to zero, then asymptotic consensus among all agents is ensured. The important feature of this transformation is that it provides input decoupling in the dynamics of the new error variables making the consensus control design a simple and direct task. Using classical Nussbaum gain based techniques, distributed controllers are designed to regulate the PI consensus error variables to zero and ultimately solve the agreement problem. The proposed approach also allows for a specific calculation of the final consensus point based on the controller parameter selection and the associated graph topology. Simulation results verify our theoretical derivations.