Genetic programming for generalised helicopter hovering control

We show how genetic programming can be applied to helicopter hovering control, a nonlinear high dimensional control problem which previously has been included in the literature in the set of benchmarks for the derivation of new intelligent controllers . The evolved controllers are compared with a neuroevolutionary approach which won the first position in the 2008 helicopter hovering reinforcement learning competition. GP performs similarly (and in some cases better) with the winner of the competition, even in the case where unknown wind is added to the dynamic system and control is based on structures evolved previously, i.e. the evolved controllers have good generalisation capability