Decoupling of a Synchronous Generator by an Adaptive Model Reference Control Based on Radial Basis Functions

摘要

An alternative nonlinear technique for decoupling and control based on a RBF (Radial Basis Functions) neural network applied to synchronous generator is presented. This technique does not require knowledge of the system and due the nature of radial basis functions, it shows itself stable to parametric uncertainties, disturbances and simpler when it is applied in control. The RBF decoupler is designed in this paper for decouple a nonlinear MIMO system with two inputs and two outputs. The weights between hidden layer and output layer are modified online using an integral law. A decoupling adaptive controller uses the error between reference input, output and filtered outputs to produce control signals to the generator, forcing its outputs to behave like the reference model. Simulation is presented to show the performance of the RBF decoupling. A brief comparison with a decoupling presented by Araujo (1983), using a Hirschorn's algorithm to find inverse system and an asymptotic functional reproducibility applied to same nonlinear model of the generator is commented.