Performance comparison of learning algorithms for system identification and control

Modelling a dynamical system is a crucial step in the design of a control law. Corresponding to a dynamical system there is a set of models, and the designer chooses one of them. In the case of nonlinear dynamical systems, a possible way to obtain a model that can be used as well for control is an artificial neural network. Accordingly, the performance of the overall system depends on how fast the network can be trained. In this paper, we compare two classes of learning algorithms and discuss their use in the context of identification and control.