An intelligent integrated navigation and control solution for an unmanned surface craft

An adaptive navigation and control algorithm is presented in this paper based on fuzzy logic and optimal control techniques and applied on an unmanned surface vehicle platform. The navigation system consists of an extended Kalman filter with time-varying parameters. Whilst the autopilots include a fuzzy logic based linear quadratic Gaussian controller and a model predictive controller optimized using a genetic algorithm. Both the controllers use the output of the adaptive navigation system as their feedback and therefore creates an integrated system. A multiple waypoint following scenario is considered and tested in real time. Experimental results are shown that demonstrate the efficacy of the proposed system.