Neuromorphic control as a self-tuning regulator

It is demonstrated that artificial neural networks can be used for the direct adaptive control of both discrete- and continuous-time nonlinear, multi-input/multi-output dynamical systems with unknown dynamics. A neuromorphic controller is presented. and its application to a self-tuning regulator problem is demonstrated. The neuromorphic controller performs functions similar to those of adaptive controllers discussed in modern control theory, with the controller taking the form of a nonlinear network and the adaptable controller parameters being the interconnection weights between neurons. In the discrete-time case the weights are adjusted by a nonlinear recursive least squares (NRLS) algorithm, and in the continuous-time case the weights are adjusted by a backpropagation algorithm