Bounded connectivity-preserving leader-follower flocking algorithms without acceleration measurements

The problem of distributed connectivity-preserving leader-follower flocking of multiple autonomous agents with second-order dynamics is investigated. First, a new class of bounded artificial potential fields is carefully designed which could guarantee connectivity preservation, distance stabilization and collision avoidance simultaneously as the system evolves. Furthermore, in the absence of acceleration measurements of the dynamic leader, a set of distributed and bounded leader-follower flocking control protocols is derived for each follower with the aid of the combination of potential based gradient descent methods and the sliding mode control paradigms. It is shown that all followers achieve velocity consensus and collision avoidance with the dynamic leader, the underlying network remains connected for all time, and the desired stable flocking behavior is asymptotically achieved on the condition that the initial network is connected. Finally, nontrivial simulations and experiments are worked out to verify the effectiveness of the proposed control algorithms.