Research on fuzzy PID Shared control method of small brain-controlled uav
This work addresses control challenges in brain-controlled UAVs for applications in brain-computer integration, but it is incremental as it builds on existing shared control concepts with a specific method.
The paper tackles the problem of poor control performance in brain-controlled UAVs due to limited BCI accuracy and command recognition by designing a fuzzy PID shared control method that combines automatic and brain control with a switching mechanism. The result is improved system control performance, validated through a rectangular trajectory tracking experiment for a small quadrotor.
Brain-controlled unmanned aerial vehicle (uav) is a uav that can analyze human brain electrical signals through BCI to obtain flight commands. The research of brain-controlled uav can promote the integration of brain-computer and has a broad application prospect. At present, BCI still has some problems, such as limited recognition accuracy, limited recognition time and small number of recognition commands in the acquisition of control commands by analyzing eeg signals. Therefore, the control performance of the quadrotor which is controlled only by brain is not ideal. Based on the concept of Shared control, this paper designs an assistant controller using fuzzy PID control, and realizes the cooperative control between automatic control and brain control. By evaluating the current flight status and setting the switching rate, the switching mechanism of automatic control and brain control can be decided to improve the system control performance. Finally, a rectangular trajectory tracking control experiment of the same height is designed for small quadrotor to verify the algorithm.