Globally stable adaptive tracking control using RBF neural networks as feedforward compensator

In this paper, it is showed that if neural networks are used as feedforward compensators instead of feedback ones, then we can ensure the global stability of closed-loop systems and determine the neural network approximation domain via the bound of known reference signals. It should be pointed out that this domain is very important for designing the neural network structure, for example, it directly determines the choice of the centers of radial basis function neural networks.