Bio-inspired optimal control via intermittent cooperation

We investigate the solution of a large class of fixed-final-state optimal control problems by a group of cooperating dynamical systems. We present a pursuit-based algorithm, inspired by the foraging behavior of ants that requires each system-member of the group to solve a finite number of optimization problems as it follows other members of the group from a starting to a final state. Our algorithm, termed "sampled local pursuit", is iterative and leads the group to a locally optimal solution, starting from an initial feasible trajectory. The proposed algorithm is broad in its applicability and generalizes previous results. It requires only short-range sensing and limited interactions between group members, and avoids the need for a "global map" of the environment or manifold on which the group evolves. We include simulations that illustrate the performance of our algorithm.