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We propose a novel approach to input design for identification of nonlinearstate space models. The optimal input sequence is obtained by maximizing ascalar cost function of the Fisher information matrix. Since the Fisherinformation matrix is unavailable in closed form, it is estimated usingparticle methods. In addition, we make use of Gaussian process optimization tofind the optimal input and to mitigate the problem of a large computationalcost incurred by the particle filter, as the method reduces the number offunctional evaluations. Numerical examples are provided to illustrate theperformance of the resulting algorithm.

Particle-based Gaussian process optimization for input design in nonlinear dynamical models