Passivity feedback equivalence of nonlinear systems via neural networks approximation

The design of a passivity feedback equivalence controller for a class of single input-single output nonlinear systems using neural network function approximation is proposed. Radial basis functions are used to synthesize the approximation of nonlinear mappings. Assuming that the uncertainty that results from this approximation is gain bounded, an adaptive technique is also used in the learning procedure of the neural network.