Quadrotors possess traits such as under-actuation, nonlinearity, and strong coupling. Quaternions are primarily used for attitude calculations in drones, with error quaternions seldom being employed directly in the control of specific quadcopter drones. This paper focuses on the low tracking accuracy and weak anti-interference ability of quadcopter drones in trajectory-tracking control. By establishing the quadcopter quaternion model, a controller based on quaternion error is designed through a combination of fractional-order PID control with S-plane control. Trajectory-tracking experiments demonstrate that, in comparison with fractional-order PID, this method exhibits strong wind disturbance resistance and high tracking accuracy.

This paper focuses on the issues of low path tracking precision and weak disturbance rejection capability in fixed‐wing unmanned aerial vehicle (UAV). This study designs a path tracking controller that combines the radial basis function (RBF) neural network and the supertwisting sliding mode control (STSMC) algorithm. The theoretical stability of the proposed controller is proved by using the Lyapunov theory. The research begins by establishing the motion model of the fixed‐wing UAV. The designed controller, integrating the RBF neural network and STSMC, is implemented to enable the UAV to focus on accurately tracking the desired path. The RBF neural network is utilized to estimate and compensate for external disturbances within the model. Validation of the proposed approach is conducted through semiphysical simulation experiments, demonstrating that the designed control method can effectively enhance anti‐interference ability and suppress chattering.

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness.