  This paper presents a novel control strategy to herd groups of
non-cooperative evaders by means of a team of robotic herders. In herding
problems, the motion of the evaders is typically determined by strongly
nonlinear and heterogeneous reactive dynamics, which makes the development of
flexible control solutions a challenging problem. In this context, we propose
Implicit Control, an approach that leverages numerical analysis theory to find
suitable herding inputs even when the nonlinearities in the evaders' dynamics
yield to implicit equations. The intuition behind this methodology consists in
driving the input, rather than computing it, towards the unknown value that
achieves the desired dynamic behavior of the herd. The same idea is exploited
to develop an adaptation law, with stability guarantees, that copes with
uncertainties in the herd's models. Moreover, our solution is completed with a
novel caging technique based on uncertainty models and Control Barrier
Functions (CBFs), together with a distributed estimator to overcome the need of
complete perfect measurements. Different simulations and experiments validate
the generality and flexibility of the proposal.
