Abstract
In this paper, the design of a control system for nonlinear plants with a time delay is proposed. It is derived that the control systems of any nonlinear plants with a time delay can be reduced to a simple second-order model. For the identification of nonlinear plants, radial basis function neural networks, which are known for their stable learning capability and fast training, are used. In the simulation study of nonlinear plants with a time delay, it was observed that the steady-stete error between the plant output and the reference model output was small. Moreover, in the experimental study of an actual pneumatic cylinder, it is shown that the proposed method yields a stable tracking performance.
