A nonlinear adaptive controller based on RBF networks

This paper proposes a nonlinear direct adaptive controller, based on radial basis function (RBF) networks. It is robust, reliable, efficient and simple. Compared with controllers based on BP networks, the proposed algorithm converges much more quickly without the problem of local minima. Simulation examples demonstrate the simplicity of the design procedure and the good characteristics of the control strategy. Moreover they illustrate that the controller possesses strong disturbance rejection and overcomes the drawback in outerpolation (accurate prediction outside the training domain) of neural network models