Coordinated deviation correction control for aircraft multi-actuators based on time-varying model predictive control
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摘要:
针对现有飞机无法高效地利用多个纠偏执行器进行协同纠偏，导致冲偏出跑道事故频发的问题，提出一种基于时变模型预测控制(MPC)的多执行器协同纠偏控制方法。建立精细的飞机地面滑跑动力学模型，并提出一种内外环分层控制方法架构；外环采用动态虚拟目标点导引律，将飞机与跑道中线的横向位置偏差转化为内环的偏航角指令；内环基于MPC方法，通过设计随滑跑速度动态调整的控制权重矩阵，在线优化求解方向舵、前轮转弯及主轮差动刹车的控制量，实现多执行器的协同控制。搭建了六自由度飞机动态仿真平台，并在多种工况下进行了验证，仿真结果表明：所提控制策略能够快速稳定地跟踪跑道中线，有效应对复杂工况，实现全速域内各纠偏执行器的平滑切换与高效协同。
Abstract:Runway excursions represent a significant percentage of aviation incidents, highlighting the critical limitations of current ground control methods, often restricted to a single actuator. This impedes the safe and synergistic use of multiple control surfaces for deviation correction of aircraft taxiing. To address the question, this paper proposes a novel coordinated multi-actuator control method based on a time-varying model predictive control (MPC) framework. The comprehensive methodology begins with a high-fidelity dynamic model of the aircraft’s ground taxiing phase, precisely considering complex nonlinear phenomena such as aerodynamic forces, ground friction dynamics, and tire side-slip characteristics. This model is then formulated as a linear time-varying state-space representation, which is suitable for the MPC framework. Building upon this model, a robust hierarchical inner-outer loop control architecture is designed for the systematic deviation correction task. Using a dynamic virtual target point guiding law, the outer loop converts the aircraft’s lateral position divergence from the runway centerline into an exact yaw angle instruction for the inner loop. The inner loop forms the core of the strategy, utilizing MPC for robust, coordinated control of the three primary actuators: rudder, nose wheel steering, and differential braking. A key innovation is a control weight matrix in the MPC cost function that adjusts dynamically with real-time taxiing speed. This adaptive weighting mechanism optimizes control inputs online, intelligently allocating authority across the full speed range. At high speeds, it gives priority to the rudder; at lower speeds, it smoothly switches to nose wheel steering and differential braking. For validation, a comprehensive 6-degree-of-freedom dynamic simulation platform was developed in MATLAB/Simulink. The strategy was rigorously tested under challenging conditions, including wet runways, crosswinds, and significant initial landing deviations. Simulation results consistently demonstrate that the controller enables fast, stable tracking of the runway centerline. The system effectively manages these complex scenarios, realizing smooth, efficient coordination among the actuators.
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图 1 飞机地面滑跑受力分析示意
Figure 1. Schematic diagram of forces acting on an aircraft during ground taxiing
图 3 纠偏控制系统架构示意
Figure 3. Schematic diagram of taxiing correction control system architecture
表 1 六自由度飞机动态系统模型参数
Table 1. Parameters of the 6-degree-of-freedom aircraft dynamic system model
飞机
质量/kg飞机对$ x $轴
的惯性矩/
(kg·m−2)飞机对$ y $轴
的惯性矩/
(kg·m−2)飞机对$ z $轴
的惯性矩/
(kg·m−2)主轮到质心
纵向距离/m前轮到质心
纵向距离/m2个主轮
间距/m飞机质心
高度/m机翼
面积/m2翼展/m 方向舵
面积/m2方向舵
展长/m前轮
质量/kg前轮
半径/m主轮
质量/kg主轮
半径/m15 119 31 184 205 125 230 414 1.048 98 15.451 02 3 3 37.16 11.41 8 5 98 0.35 117 0.4 
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表 2 控制器参数
Table 2. Parameters of the controller
$ {k}_{\mathrm{i}} $ $ {l}_{0} $/m $ {T}_{\text{s}} $/s $ P $ $ N $ $ \boldsymbol{Q} $ $ {\mu }_{y\text{m}} $ $ {\mu }_{y\text{nw}} $ 最大前轮转角/(°) 最大方向舵转角/(°) 最大差动刹车压力值/MPa 1.5 15 0.001 200 20 [100,0.01,0.01]T 1 567 748 200 000 15 30 7
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表 3 仿真初始参数
Table 3. Simulation initial parameters
滑跑初始速度/(km·h−1) 初始距跑道中线距离/m 初始前轮转角/(°) 初始方向舵转角/(°) 初始左右刹车压力/MPa 初始侧向速度/(km·h−1) 200 10 0 0 3 0
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