Single-Agent Indirect Herding of Multiple Targets With Uncertain Dynamics
Ryan A. Licitra, Zachary I. Bell, Warren E. Dixon · IEEE Transactions on Robotics · 2019
In this paper, an indirect herding problem is considered for a single herder that is outnumbered by multiple target agents. Indirect herding is a problem that involves a network of one or more controllable herding agents and indirectly controlled target agents (i.e., the target agents can only be controlled through the herder by exploiting herder-target interactions), where the goal is to achieve a network-wide objective. This paper investigates the unique problem where a single herder is required to regulate all the target agents to some desired formation. The problem is further complicated by the fact that the target agents have uncertain nonlinear dynamics, including uncertainties in the herder-target interaction. Neural network function approximation methods are used along with switched systems methods to ensure uniformly ultimately bounded convergence of the agent trajectories provided the developed sufficient dwell-time conditions are satisfied. Simulation and experimental results that involve one herding agent and multiple target agents demonstrate the validity of the designed controller and function approximation scheme for multiple herding objectives.