With its flexibility in operation, rapid inspection capability, omnidirectional detection, and strong terrain adaptability, the quadrotor has become an effective tool for underground inspection. The underground environment is characterized by narrow passages and turbulent airflow. When encountering strong gusts in underground spaces, the quadrotor maintains a large attitude angle to ensure stability. There is an significant model deviation between the actual dynamic model of the quadrotor and the hovering dynamic model. In general, the controller of the quadrotor is designed based on the hovering dynamic model in still air. The deviation between the model in the controller and the actual model can lead to the loss of control of the quadrotor. To overcome the problem above, at the environmental perception level, a real-time wind field prediction model is constructed based on the information obtained by wind speed sensors. By integrating this predicted information with an improved adaptive extended set membership filtering algorithm, a multi-step incremental prediction mechanism is introduced. At the control architecture level, a dynamic model compensation mechanism is proposed for nonlinear system characteristics. By identifying model bias online, an active model correction strategy is established. The incremental predictive control value is used as the feedforward compensation input, and an optimal control problem with constraints is established. Finally, a simulation example is presented to verify the effectiveness of the quadrotor control method.
Zhao et al. (Wed,) studied this question.