To control a humanoid locomotion, much work can be found in the literature that has been focused on the Zero Moment Point approach. More recently, biologically inspired control strategies such as Central Pattern Generators have been proposed to generate autonomously adaptable rhythmic movement. Despite the extensive research works in this area, suitable autonomous control system that can adapt and interact safely with the surrounding environment while delivering high robustness is yet to be discovered. In this paper, therefore, we deal with the design of oscillatory neural network for bipedal motion pattern generator and locomotion controller. Locomotion pattern will be generated by a neural network that represents the lower layer of the overall control system of the humanoid. The neural network will be augmented by neural controllers with sensory connections to maintain the stability of the system. Moreover, we investigate the stability of the system simplified as an inverted pendulum and we propose analytical method on how to decide the parameters of the motion To validate the theoretical results, we use the humanoid robots "HOAP-3".