Path tracking controller of an autonomous armoured vehicle using modified Stanley controller optimized with particle swarm optimization

Abstract

This study presents the development and optimization of a proposed path tracking controller for an autonomous armoured vehicle. A path tracking control is developed based on an established Stanley controller for autonomous vehicles. The basic controller is modified and applied on a non-linear, 7degree-of-freedom armoured vehicle model, and consists of various modules such as handling model, tire model, engine, and transmission model. The controller is then optimized using particle swarm optimization algorithm to select the optimum set of controller parameters. The main motivation of this study is that implementation of path tracking control on an autonomous armoured vehicle is still very limited and it is important to have a specific study on this field due to the different dynamics and properties of the armoured vehicle compared to normal passenger vehicles. Several road courses are considered and the performance of the developed controller in guiding the vehicle along these courses was compared against the original Stanley Controller. It was found that the optimized controller managed to improve the overall lateral error throughout the courses with 24–96% reduction in lateral error. Also, the optimization for the proposed controller was found to converge faster than its counterpart with up to 93% better solution.