NEUROMORPHIC ARCHITECTURES FOR FAST ADAPTIVE ROBOT CONTROL.

Abstract

An architecture for an adaptive neuromorphic system designed to control a robot is suggested. The proposed architecture utilizes two important features of neural networks: the abundance of local minima in the network's state space and the uniformity of convergence of these minima in the face of growing dimensionality. The proposed approach is expected to yield controllers which are both faster and simpler than controllers which are designed by the methods of model reference adaptive control and self tuning regulator. The controller's complexity is expected not to grow exponentially with the number of unknown parameters, and to allow adaptation in both continuous and discrete parameter domains. The possible benefits of the architecture are demonstrated on a single-degree-of-freedom manipulator, whose controller is assisted by a neural estimator.