Bio-Inspired Cooperative Optimal Control with Partially-Constrained Final State

Abstract

Inspired by the process by which ants gradually optimize their foraging trails, this report investigates the cooperative solution of a class of free-final time, partially-constrained final state optimal control problems by a group of dynamic systems. A class of cooperative, pursuit-based algorithms are proposed for finding optimal solutions by iteratively optimizing an initial feasible control. The proposed algorithms require only short-range, limited interactions between group members, avoid the need for a ``global map'' of the environment on which the group evolves, and solve an optimal control problem in ``small'' pieces, in a manner which will be made precise. The performance of the algorithms is illustrated in a series of simulations and laboratory experiments.