A new analog control circuit design for hexapod using cellular neural network

This paper presents a new design of cellular neural network for controlling hexapod robot movement on forward motion. It shows a set of new state equations which higher propagation than the original one. These new equations reduce number of CNN pattern generation cells from 12 to 6 cells. As a result, this provides a new technique for designing CNN analog control circuits which can be reduced circuit components and simplified the system architectures.