Abstract
Indirect herding involves using a set of directly controllable herding agents to indirectly regulate the states of target agents. This letter considers a single herding agent tasked with regulating the states of multiple targets that are repelled from the herder and the goal location. Integral concurrent learning is used to develop an exponentially convergent adaptive controller that is proven to achieve globally uniformly ultimately bounded regulation through a Lyapunov-based analysis. Switched systems methods are used to develop dwell-time conditions that direct the herder to switch between targets. Experimental results with a network of quadcopters are included to illustrate the performance of the developed controller.
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