On output regulation for SISO nonlinear systems with dynamic neural networks

In this work, the output regulation theory is combined with a dynamic neural identifier, in order to improve the robustness properties for trajectory tracking on SISO nonlinear system's. A neural network is used to identify the dynamics of the nonlinear system, by a suitable on-line training, which ensures small identification error. Then, the output regulation technique is applied to the neural network to obtain a controller that, when applied to the original system, guarantee also a bounded output tracking error despite the presence of parameter variations and external perturbations. Simulation results on a model of a chaotic system are presented showing the viability and effectiveness of the proposed technique.