1991 Volume 4 Issue 1 Pages 37-47
In this paper, we propose a new learning scheme using feedback-error-learning for a Neural network model applied to an adaptive Nonlinear Feedback Controller (NNFC). This system uses a Conventional Feedback Controller (CFC) both as a usual feedback controller to guarantee global asymptotic stability and as a reference model of the response. The output of the conventional feedback controller is used as the error signal for a neural network model for adaptive nonlinear feedback control. The response of the controlled object follows the response of the reference model after the learning period.
The convergence properties of this learning scheme are provided by using the averaged equation and the Liapunov method. This scheme was successfully applied to the control of an inverted pendulum by computer simulation. We also pointed out the relationship of this learning scheme to the cerebellum's posture and locomotion adaptive control mechanism in animals.