Bobo Ye

Dynamic derivatives are critical for evaluating an aircraft’s aerodynamic characteristics, dynamic modeling, and control system design during the design phase. However, due to the multiple iterations of the design phase, a method for calculating dynamic derivatives that balances computational efficiency and accuracy is required. This work presents a CFD-enhanced engineering calculation method (CEHM) for calculating tandem-wing UAVs’ dynamic derivatives. A coupling-effect-driven estimation strategy is proposed to incorporate the contribution of the rear wing to the longitudinal dynamic derivatives, and it accounts for the aerodynamic coupling effects between the front and rear wings. To enhance the accuracy of the dynamic derivative calculations, we put forward a dynamic derivative-correction mechanism based on the CFD method. It achieves three types of parameters from the static derivative CFD simulations to enhance accuracy, including parameters for aerodynamic force coefficient fitting, the dynamic pressure ratio, and the upwash and downwash gradients. The CEHM method is applied to compute the dynamic derivatives of the SULA90 tandem-wing UAV, with results compared to those obtained from the traditional engineering estimation tools (XFLR5 and OpenVSP). The simulation experiment results show that the proposed method not only calculates the acceleration derivatives but also provides higher calculation accuracy. To further validate the method’s effectiveness, open-loop model verifications were conducted using field flight test data of the SULA90. The field flight test results show that the CEHM method’s predicted results align closely with the measured flight data. The proposed method calculates dynamic derivatives in seconds, balancing accuracy and computational cost, making it highly suitable for tandem-wing aircraft during the design phase. Furthermore, this approach is generalizable and can be applied to other aircraft configurations.

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs.

Next-generation unmanned aerial vehicles (UAVs) should possess the capability to autonomously perform multiple tasks with agility and efficiency in complex obstructive environments and over open terrains. Morphing UAVs can autonomously transform in response to changes in flight environments and tasks, always maintaining an optimal aerodynamic profile. The falcon-inspired morphing UAV folds/twists its wings/tail simultaneously to accomplish diving/pull-out flight during the predation process. In the diving/pull-out flight, falcon-inspired morphing UAVs are able to balance maneuverability and stability, which is hard to realize by current control methods. This paper proposes a deep reinforcement learning (DRL)-based diving/pull-out cooperative control strategy. Considering the continuity of the state space and the action space of morphing UAVs, the deep deterministic policy gradient (DDPG) algorithm based on the actor-critic (AC) network is adopted and refined. With the aim of ensuring the smoothness of the flight action, the proposed DRL-based strategy is tasked with controlling multiple data frames of airspeed, altitude and pitch angle to desired reference values. Numerical experiments have been conducted on fixed-speed ascent/descent flight and diving/pull-out maneuvers flight missions. The results demonstrate the superiority of the proposed DRL-based control strategy compared with a classical proportional-integral-derivative (PID) control strategy. Furthermore, the proposed DRL controller is shown to generalize well to the random white noises which are added to gyroscope measurements and wind disturbance in flight.