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摘要:
针对欠驱动与参数不确定性耦合导致的固定翼无人机控制设计与稳定性分析难题，提出一种基于触发式批处理辨识的自适应分层控制方法。为处理欠驱动问题，基于独轮车模型将原固定翼无人机模型转换为虚拟积分器与线性参数化模型；结合反步法与固定时间控制技术，建立等效飞行跟踪控制器。通过分析参数不确定性对无人机运动状态的作用机理，采用谱分解技术构造线性回归方程以定量收集激励信息，确保收集激励信息的丰富性和线性回归方程的有界性。为消除参数不确定性的影响，建立触发式批处理参数估计器，在触发函数满足特定条件时充分利用已收集的激励信息更新参数估计。与固定翼无人机的现有分层控制策略相比，所提框架实现了误差收敛性、激励丰富性与参数辨识精度的统一。
Abstract:In this paper, an adaptive hierarchical control method based on a regulation-triggered batch identifier is proposed. This approach addresses the challenges in stability analysis and control design of fixed-wing UAVs, which are complicated by the coupling of underactuated characteristics and parametric uncertainty. In order to deal with the underactuated problem, the original fixed-wing UAV model is transformed into a virtual integrator and linear parameterized model based on the unicycle model. Backstepping is then combined with fixed-time control methods to create an equivalent flight tracking controller. By analyzing the mechanism of parameter uncertainty on the motion state of the UAV, a linear regression equation is constructed by spectral decomposition technology to quantitatively collect excitation information, which ensures the richness of the collected excitation information and the boundedness of the linear regression equation. A regulation-triggered batch identifier is created to update the parameter estimation using the gathered excitation data when the trigger function satisfies specific requirements in order to remove the impact of parametric uncertainty. Compared with the existing hierarchical control strategies for fixed-wing UAVs, the proposed framework achieves the unity of error convergence, excitement richness and parameter identification accuracy.
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图 6 参数估计$ \hat{\boldsymbol{\theta }} $
Figure 6. Parameter estimate $ \hat{\boldsymbol{\theta }} $
表 1 激励收集方式对比
Table 1. Comparison of excitation collection methods
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