  In this paper, we consider the problem of dynamically tuning gains for
multi-robot systems (MRS) under potential based control design framework where
the MRS team coordinates to maintain a connected topology while equipped with
limited field of view sensors. Applying the potential-based control framework
and assuming robot interaction is encoded by a triangular geometry, we derive a
distributed control law in order to achieve the topology control objective. A
typical shortcoming of potential-based control in distributed networks is that
the overall system behavior is highly sensitive to gain-tuning. To overcome
this limitation, we propose a distributed and adaptive gain controller that
preserves a designed pairwise interaction strength, independent of the network
size. Over that, we implement a control scheme that enables the MRS to be
resilient against exogenous attacks on on-board sensors or actuator of the
robots in MRS. In this regard, we model additive sensor and actuator faults
which are induced externally to render the MRS unstable. However, applying
$H_{\infty}$ control protocols by employing a static output-feedback design
technique guarantees bounded $L_2$ gains of the error induced by the sensor and
actuator fault signals. Finally, we apply policy iteration based Q-Learning to
solve for adaptive gains for the discrete-time MRS. Simulation results are
provided to support the theoretical findings.
