摘要
针对可变形四旋翼无人机在快速变形飞行过程中易受转动惯量时变、下洗气动效应变化、外部扰动和执行器硬约束共同影响而导致轨迹跟踪精度下降的问题，本文提出一种融合扩张状态观测器（Extended State Observer, ESO）与非线性模型预测控制（Nonlinear Model Predictive Control, NMPC）的高精度抗扰控制方法。首先，建立包含臂长相关转动惯量、下洗气动阻力系数和时变力矩输入增益的控制导向动力学模型，并通过臂长名义动力学辨识模型实现由变形指令到预测模型参数的在线更新。然后，构建位置环和姿态环串级控制器，利用ESO在线估计参数失配、气动不确定性、外部扰动及环间耦合形成的集中扰动，利用NMPC在滚动优化中显式处理速度、姿态定义域和单旋翼推力等物理约束，并采用五次多项式规划生成平滑臂长轨迹，以协调变形执行与模型刷新。理论分析证明了该分级架构下的闭环系统稳定性及误差有界性。最后，通过算法级数字仿真、MuJoCo 物理引擎仿真和原型机飞行实验进行了全面验证。结果表明，所提方法不仅有效保证了预测优化的实时性，还大幅提升了系统对极端变形速率与复合扰动的鲁棒适应能力。
关键词： 可变形四旋翼无人机；扩张状态观测器；非线性模型预测控制；抗扰控制
Abstract
This paper proposes a high-precision disturbance-rejection control scheme integrating an Extended State Observer (ESO) with Nonlinear Model Predictive Control (NMPC) to address the degradation in trajectory tracking accuracy of morphing quadrotor UAVs during rapid morphing flights. This degradation is primarily induced by the combined effects of time-varying moments of inertia, varying downwash aerodynamics, external disturbances, and hard actuator constraints. First, a control-oriented dynamics model is developed, incorporating arm-length-dependent inertia, downwash-induced drag ratios, and time-varying torque input gains. A nominal identification model for arm-length dynamics enables the online updating of predictive model parameters based on morphing commands. Subsequently, a cascaded position-attitude controller is designed. The ESO estimates lumped disturbances online, arising from parameter mismatches, aerodynamic uncertainties, external perturbations, and inter-loop coupling. Meanwhile, the NMPC explicitly enforces physical constraints, including velocity limits, attitude boundaries, and individual rotor thrust, within the receding-horizon optimization. Additionally, fifth-order polynomial trajectory planning generates smooth arm-length profiles to synchronize morphing execution with model parameter updates. Theoretical analysis proves the stability and error boundedness of the closed-loop system under this hierarchical architecture. Finally, the proposed approach is comprehensively validated through numerical simulations, MuJoCo-based physics simulations, and prototype flight experiments. Results confirm that the proposed method not only effectively guarantees the real-time performance of the predictive optimization but also significantly enhances the system's robustness against extreme morphing rates and composite disturbances.
Key words： Morphing quadrotor UAV; Extended state observer; Nonlinear model predictive control; Disturbance-rejection control
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