抄録
This paper presents a new structure of nonlinear adaptive control system incorporating a neural network (NN). The control input is given by the sum of the output of a robust adaptive controller and the output of the NN. The role of the NN is to compensate for constructing a linearized model so as to minimize an output error caused by nonlinearities in the controlled system. The role of the robust adaptive controller is to perform the model-matching for the uncertain linearized system to a given linear reference model. One of the distinctive features of the proposed structure is to give an efficient method for calculating the derivative of the system output with respect to the input by using one identified parameter in the linearized model and the internal variables of the NN, which enables to perform the back-propagation algorithm very efficiently. In numerical simulations, we examine the effectiveness of the proposed scheme by applying it to a variety of nonlinear systems, such as a system with backlash.
