Adaptive modified Stanley controller with fuzzy supervisory system for trajectory tracking of an autonomous armoured vehicle

In developing path tracking controller for autonomous vehicles, a properly tuned controller will work well for a certain range of driving conditions but may need to be re-tuned for others. This study presents the development of an adaptive controller with fuzzy supervisory system for trajectory tracking control of an autonomous armoured vehicle. A knowledge database is built using Particle Swarm Optimisation which is the mainframe of the Fuzzy supervisory system in adapting to various trajectories and speed. The proposed controller is simulated on a nonlinear vehicle model, and experimental results for the controller are presented to evaluate the proposed controller.