  In this paper, we introduce a novel architecture to connecting adaptive
learning and neural networks into an arbitrary machine's control system
paradigm. Two consecutive Recurrent Neural Networks (RNNs) are used together to
accurately model the dynamic characteristics of electromechanical systems that
include controllers, actuators and motors. The age-old method of achieving
control with the use of the- Proportional, Integral and Derivative constants is
well understood as a simplified method that does not capture the complexities
of the inherent nonlinearities of complex control systems. In the context of
controlling and simulating electromechanical systems, we propose an alternative
to PID controllers, employing a sequence of two Recurrent Neural Networks. The
first RNN emulates the behavior of the controller, and the second the
actuator/motor. The second RNN when used in isolation, potentially serves as an
advantageous alternative to extant testing methods of electromechanical
systems.
