This article describes an empirical approach to nonlinear control problems that calls upon the evolution of modular neural networks. This approach may be bootstrapped with modules that encode knowledge stemming from linear or nonlinear control theory, and it seems to be applicable to nonstationary problems as well. It has been applied here to the control of the trim and altitude of a simulated lenticular blimp that was subjected to several perturbations. The corresponding results demonstrate the superiority of the evolved networks over a hand-designed controller. They also demonstrate the capacity of evolution to exploit the intrinsic nonlinearities of artificial neurons in order to generate different solutions, likely to be adapted to the context of the considered application.