Neuromorphic Pitch Attitute Regulation of an Underwater Telerobot

Abstract

Previous research performed in the Space Systems Laboratory has demonstrated the feasibility of teaching neural networks to regulate dynamic systems. This neuromorphic control algorithm is a direct application of the back propagation entrainment method, with those modifications required to pose the problem in a control systems framework. Prior work has been limited to computer simulation of the properties of the regulators developed by these networks: this paper presents the experimental results of using trained neural networks to regulate the pitch attitute of an underwater telerobot. The networks perform as predicted by the simulation results, however it is observed that the complexity of the calculations required can create unacceptable delays when using a single serial microprocessor to compute the control. This problem becomes especially acute when scalling the architecture to more complex neural topologies. Special purpose hardware, which directly implements the neural equations and hence realizes the benefits of the natural parallelism of these models, is seen as a necessary development for the effective use of neural controllers.