Online parameter adaptive control of mobile robots based on deep reinforcement learning under multiple optimisation objectives
Xinhang Sui, Haiyong Chen · Cognitive Computation and Systems · 2024
Abstract Fixed control parameters and various optimisation objectives significantly limit the robot control performance. To address such issues, a parameter adaptive controller based on deep reinforcement learning is introduced firstly to adjust control parameters according to the real‐time system state. Further, multiple evaluation mechanisms are constructed to take account of optimisation objectives so that the controller can adapt to different control performance indexes by different evaluation mechanisms. Finally, the target pedestrian tracking control with mobile robots is selected as the validation case study, and the Proportional‐Derivative Controller is chosen as the foundation controller. Several simulation and experimental examples are designed, and the results demonstrate that the proposed method shows satisfactory performance while taking account of multiple optimisation objectives.