This paper designs a robust close-formation control system with dynamic estimation and compensation to advance unmanned aerial vehicle (UAV) close-formation flights to an engineer-implementation level. To characterize the wake vortex effect and analyze the sweet spot, a continuous horseshoe vortex method with high estimation accuracy is employed to model the wake vortex. The close-formation control system will be implemented in the trailing UAV to steer it to the sweet spot and hold its position. Considering the dynamic characteristics of the trailing UAV, the designed control system is divided into three control subsystems for the longitudinal, altitude, and lateral channels. Using linear active-disturbance rejection control (LADRC), the control subsystem of each channel is composed of two cascaded first-order LADRC controllers. One is responsible for the outer-loop position control and the other is used to stabilize the inner-loop attitude. This control system scheme can significantly reduce the coupling effects between channels and effectively suppress the transmission of disturbances caused by the wake vortex effect. Due to the cascade structure of the control subsystem, the correlation among the control parameters is very high. Therefore, sine-powered pigeon-inspired optimization is proposed to optimize the control parameters for the control subsystem of each channel. The simulation results for two UAV close formations show that the designed control system can achieve stable and robust dynamic performance within the expected error range to maximize the aerodynamic benefits for a trailing UAV.

To solve the self-alignment problem of strapdown inertial navigation system (SINS) with geographical latitude uncertainty, an optimization-based self-alignment (OSA) method and its improvement for stationary SINS without using the latitude information are proposed. We use only the accelerometer and gyroscope measurements, without the aid of the external latitude information, to determine the Earth rate in the navigation frame. Then we formulate the SINS self-alignment process as a Wahba problem to overcome the disturbances of random noise, and use the estimated Earth rate vector and multiple measurements from the accelerometer and gyroscope to formulate the nonlinear objective function. Moreover, the alignment errors of the OSA method are also presented, based on which we propose a two-position OSA to estimate and compensate the horizontal accelerometer biases to further improve the alignment accuracy. The results of simulation and experiments demonstrate that the proposed OSA method and its improvement perform robust to the noise disturbances and achieve better alignment accuracy than conventional self-alignment methods.

No abstract

No abstract

No abstract