CMAC neural network architecture for control of an autonomous undersea vehicle
Rick F. Comoglio, Abhijit S. Pandya · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
The design of an autonomous undersea vehicle (AUV) control system is a significant challenge in light of the highly uncertain nature of the ocean environment together with partially known nonlinear vehicle dynamics. This paper describes a neural network architecture called Cerebellar Model Arithmetic Computer (CMAC). CMAC is used to control a model of an autonomous underwater vehicle. The AUV model consists of two input parameters, the rudder and stern plane deflections, controlling six output parameters; forward velocity, vertical velocity, pitch angle, side velocity, roll angle, and yaw angle. Properties of CMAC and results of computer simulations for identification and control of the AUV model are presented.