Neural net versus control theory

The authors present some results on neural controllers. They summarize five neural controller architectures, which can be divided into two classes: general learning and special learning. In the general learning architecture examples, one neural net is trained to simulate the PUMA 560 inverse kinematics and another neural net is used to control the PUMA 560 writing in chalk. The authors then use control theory to analyze special learning architectures. It is found that a simple PID (proportional plus integral plus derivative) controller can provide a momentum factor in backpropagation (BP) learning laws. However, it is found that momentum may make BP convergence unstable, but that one can redesign the PID controller to avoid this problem