Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances

Sensors

Cheongwoong Kang¹  Bumjin Park¹  Jaesik Choi¹ 

[1] Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea;

关键词: waypoint tracking;    external disturbance estimation;    quadrotor control;    reinforcement learning;    deep learning;    artificial intelligence;   

DOI  :  10.3390/s22010150

来源: DOAJ

【 摘 要 】

Recently, the use of quadrotors has increased in numerous applications, such as agriculture, rescue, transportation, inspection, and localization. Time-optimal quadrotor waypoint tracking is defined as controlling quadrotors to follow the given waypoints as quickly as possible. Although PID control is widely used for quadrotor control, it is not adaptable to environmental changes, such as various trajectories and dynamic external disturbances. In this work, we discover that adjusting PID control frequencies is necessary for adapting to environmental changes by showing that the optimal control frequencies can be different for different environments. Therefore, we suggest a method to schedule the PID position and attitude control frequencies for time-optimal quadrotor waypoint tracking. The method includes (1) a Control Frequency Agent (CFA) that finds the best control frequencies in various environments, (2) a Quadrotor Future Predictor (QFP) that predicts the next state of a quadrotor, and (3) combining the CFA and QFP for time-optimal quadrotor waypoint tracking under unknown external disturbances. The experimental results prove the effectiveness of the proposed method by showing that it reduces the travel time of a quadrotor for waypoint tracking.

【 授权许可】

Unknown