Abstract: In this paper, we proposed a method to design a
model-following adaptive controller for linear/nonlinear plants.
Radial basis function neural networks (RBF-NNs), which are known
for their stable learning capability and fast training, are used to
identify linear/nonlinear plants. Simulation results show that the
proposed method is effective in controlling both linear and nonlinear
plants with disturbance in the plant input.
Abstract: This paper presents a novel control method based on radial basis function networks (RBFNs) for chaotic dynamical systems. The proposed method first identifies the nonlinear part of the chaotic system off-line and then constructs a model-following controller using only the estimated system parameters. Simulation results show the effectiveness of the proposed control scheme.