New results on cooperative multi-vehicle deterministic learning control: Design and validation in gazebo simulation
Date of Original Version
In this paper, new results on the cooperative deterministic learning (CDL) control method originally proposed in  for a group of unicycle-type ground vehicles are presented by considering a generalized nonholonomic uncertain vehicle dynamics. The new controller is capable of (i) controlling the vehicles to their respective desired reference trajectories; (ii) locally accurately learning/identifying, during the real-time control process, the vehicle's uncertain dynamics using radial basis function neural networks; and (iii) re-utilizing the learned knowledge to control the multi-vehicle system with guaranteed control performance and significantly reduced computational complexity. In addition, a Gazebo-based simulator is developed, based on which simulation validations have been conducted for the proposed algorithm.
IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Dong, Xiaonan, Xiaotian Chen, Chengzhi Yuan, and Paolo Stegagno. "New results on cooperative multi-vehicle deterministic learning control: Design and validation in gazebo simulation." IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020-July, (2020): 1413-1418. doi:10.1109/AIM43001.2020.9158929.