In order to achieve the precise and agile homing control of the powered parafoil system, an optimal‐multiphase homing methodology is explored in this article. The proposed methodology is composed by a trajectory optimization method based on quantum genetic algorithm and a trajectory tracking method based on an improved active disturbance rejection control (ADRC). First, an improved optimization method combining the optimal and multiphase theory is proposed. The optimized trajectory consists of the simple trajectories, the standard lines and circles, while also satisfying the multiple constraints such as the wind disturbance, terrain avoidance, flared landing, and so on. Then, an improved active disturbance rejection controller is presented. For improving the agility of the system during the path switching and the disturbance rejection ability in windy environment, based on ADRC, a disturbance rejection control algorithm is designed for the horizontal controller of the system. By analyzing the aerodynamic characteristic of the system, the wind disturbance will be compensated previously with a feedforward compensation unit. It will be largely reduce the observation error of the extended state observer, while improving the control effect and the reaction speed. Finally, the hardware‐in‐the‐loop simulation is presented to prove the effectiveness of the proposed methodology. The results show that the optimization method can be achieved successfully and the optimized trajectory can be tracked by the improved ADRC controller precisely and agilely. The landing error is less than 15 m with the proposed homing methodology.

中文翻译：

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动力翼型系统的最优多相归位方法

为了实现对动力翼型系统的精确而灵活的归位控制，本文探索了一种最优的多相归位方法。该方法由基于量子遗传算法的轨迹优化方法和基于改进的主动干扰抑制控制（ADRC）的轨迹跟踪方法组成。首先，提出了一种结合最优和多相理论的改进优化方法。优化的轨迹由简单的轨迹，标准的直线和圆组成，同时还满足诸如风扰，避免地形，张开着陆等多重约束。然后，提出了一种改进的有源干扰抑制控制器。为了提高系统在路径切换过程中的敏捷性和大风环境下的抗干扰能力，基于ADRC，针对系统的水平控制器设计了一种抗干扰控制算法。通过分析系统的空气动力学特性，可以使用前馈补偿单元预先补偿风的干扰。它将大大减少扩展状态观察者的观察误差，同时提高控制效果和反应速度。最后，提出了硬件在环仿真，以证明所提方法的有效性。结果表明，改进的ADRC控制器能够成功，成功地实现优化方法，并能准确，准确地跟踪优化轨迹。