Consensus-Based Leader-Follower Formation Tracking for Control-Affine Nonlinear Multiagent Systems

TL;DR

This paper develops consensus-based control laws for multiagent formation tracking of control-affine nonlinear systems, extending beyond traditional linear or feedback-linearized models, and validates the approach through numerical simulations in robotics.

Contribution

It introduces a novel formation tracking control method for control-affine nonlinear multiagent systems, broadening applicability beyond linearized models.

Findings

The proposed control law achieves local asymptotic stability.

Numerical simulations demonstrate effective formation tracking in robotics.

Comparison shows improved performance over optimization-based methods.

Abstract

In the typical multiagent formation tracking problem centered on consensus, the prevailing assumption in the literature is that the agents' nonlinear models can be approximated by integrator systems, by their feedback-linearized equivalents, or by dynamics composed of deterministic linear and nonlinear terms. The resulting approaches associated with such assumptions, however, are hardly applicable to general nonlinear systems. To this end, we present consensus-based control laws for multiagent formation tracking in finite-dimensional state space, with the agents represented by a more general class of dynamics: control-affine nonlinear systems. The agents also exchange information via a leader-follower communication topology modeled as an undirected and connected graph with a single leader node. By leveraging standard tools from algebraic graph theory and Lyapunov analysis, we first derive a locally asymptotically stabilizing formation tracking law. Next, to demonstrate the effectiveness of our approach, we present results from numerical simulations of an example in robotics. These results -- together with a comparison of the formation errors obtained with our approach and those realized via an optimization-based method -- further validate our theoretical propositions.