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A genetic algorithm based nonlinear guidance and control system for an uninhabited surface vehicle

This paper reports the design of a novel guidance and control (GC) system based on nonlinear model predictive control (NMPC) for use in an uninhabited surface vehicle. The NMPC combines a recurrent neural-network model and a genetic-algorithm optimiser. The paper forms part of an on-going study funded from EPSRC Grant EP/1012923/1. M Couch (Martin.Couch@utas.utc.com), Director, UTAS, industrial collaborator, states: "This work has produced an innovative design for a guidance and control system. Its level of sophistication and generic structure makes the system an exceptionally viable product for exploitation in a number of autonomous vehicle applications especially in the Defence Industry".