Direct neural control system: Nonlinear extension of adaptive control

Abstract

The methodology of design of a conventional model-reference-adaptive-control system is extended to design a direct neural control for a class of nonlinear system with structural uncertainty. A structured feedforward neural network, a Sigmoid-linear network, is used as the controller, which can be interpreted as a nonlinear extension of the conventional adaptive control. Without a specific pretraining stage, the weights of the neural network are adjusted online to minimize the error between the plant output and the desired output signal, according to a learning law derived in light of gradient-descent method. The local stability can be achieved provided that proper conditions are satisfied for the system. Simulation studies are carried out for linear and nonlinear plants, respectively, and verify the applicability of the proposed control strategy.