Iterative Model Predictive Control for Automatic Carrier Landing of Carrier-Based Aircrafts Under Complex Surroundings and Constraints

doi:10.1007/s12204-023-2690-z

Abstract

Abstract: This paper considers the automatic carrier landing problem of carrier-based aircrafts subjected to constraints, deck motion, measurement noises, and unknown disturbances. The iterative model predictive control (MPC) strategy with constraints is proposed for automatic landing control of the aircraft. First, the long shortterm memory (LSTM) neural network is used to calculate the adaptive reference trajectories of the aircraft. Then the Sage-Husa adaptive Kalman filter and the disturbance observer are introduced to design the composite compensator. Second, an iterative optimization algorithm is presented to fast solve the receding horizon optimal control problem of MPC based on the Lagrange’s theory. Moreover, some sufficient conditions are derived to guarantee the stability of the landing system in a closed loop with the MPC. Finally, the simulation results of F/A-18A aircraft show that compared with the conventional MPC, the presented MPC strategy improves the computational efficiency by nearly 56% and satisfies the control performance requirements of carrier landing.

Key words: automatic carrier landing, model predictive control (MPC), long short-term memory (LSTM) neural network, stability, computational efficiency

摘要： 本文研究了舰载机在约束条件、甲板运动、测量噪声和未知干扰下的自动着舰问题。针对飞机的自动着陆控制问题，提出了考虑约束的迭代模型预测控制(MPC)策略。首先，利用LSTM神经网络计算飞机的自适应参考轨迹。然后引入Sage-Husa自适应卡尔曼滤波器和扰动观测器设计复合补偿器。其次，基于拉格朗日理论，提出了一种快速求解MPC滚动时域最优控制问题的迭代优化算法。在此基础上，给出了保证MPC控制下着陆系统稳定性的充分条件。最后，基于F/A-18A舰载机的仿真结果表明，本文所提出的MPC策略与传统的MPC策略相比计算效率提高近56%，满足舰载机着陆的控制性能要求。

关键词: 自动着舰，模型预测控制，LSTM神经网络，稳定性，计算效率

CLC Number:

V323.19

ZHANG Xiaotian¹(张啸天), HE Defeng^1* (何德峰), LIAO Fei² (廖飞). Iterative Model Predictive Control for Automatic Carrier Landing of Carrier-Based Aircrafts Under Complex Surroundings and Constraints[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 712-724.

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