  Affine formation control is a subset of formation control methods, which has
gained increasing popularity for its flexibility and maneuverability in diverse
applications. Affine formation control is inherently distributed in nature,
where the local controllers onboard each agent are linearly dependent on the
relative position measurements of the neighboring agents. The unavailability of
these measurements in practice, due to node failure or missing links, leads to
a change in the underlying graph topology, and subsequently causes instability
and sub-optimal performance. In this paper, we propose an estimation framework
to enhance the robustness of distributed affine formation control systems
against these topology changes. Our estimation framework features an adaptive
fusion of both temporal information from the dynamics of agents and spatial
information which is derived from the geometry of the affine formations. We
propose a suite of algorithms under this framework to tackle various practical
scenarios, and numerically verify our proposed estimator on stability,
convergence rate, and optimality criterion. Simulations show the performance of
our proposed algorithms as compared to the state-of-the-art methods, and we
summarize them with future research directions.
