复杂环境及约束下舰载机自动着舰迭代模型预测控制

doi:10.1007/s12204-023-2690-z

摘要/Abstract

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

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

中图分类号:

V323.19

张啸天1，何德峰1，廖飞2. 复杂环境及约束下舰载机自动着舰迭代模型预测控制[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(4): 712-724.

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.

参考文献

[1] ZHEN Z Y, WANG X H, JIANG J, et al. Research progress in guidance and control of automatic carrier landing of carrier-based aircraft [J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(2): 122-143 (in Chinese).
[2] ZHEN Z Y, JIANG S Y, MA K. Automatic carrier landing control for unmanned aerial vehicles based on preview control and particle filtering [J]. Aerospace Science and Technology, 2018, 81: 99-107.
[3] BHATIA A K, JU J A, KUMAR A, et al. Adaptive preview control with deck motion compensation for autonomous carrier landing of an aircraft [J]. International Journal of Adaptive Control and Signal Processing, 2021, 35(5): 769-785.
[4] ZHEN Z Y, TAO G, YU C J, et al. A multivariable adaptive control scheme for automatic carrier landing of UAV [J]. Aerospace Science and Technology, 2019, 92: 714-721.
[5] WU Q L, ZHU Q D, HAN S A. Elman neural networkbased direct lift automatic carrier landing nonsingular terminal sliding mode fault-tolerant control system design [J]. Computational Intelligence and Neuroscience, 2023, 2023: 3560441.
[6] BIAN Q, NENER B, WANG J P, et al. A fitness sharing based ant clustering method for multimodal optimization of the aircraft longitudinal automatic carrier landing system [J]. Aerospace Science and Technology, 2022, 122: 107392.
[7] YANG Z Y, DUAN H B, FAN Y M, et al. Automatic Carrier Landing System multilayer parameter design based on Cauchy Mutation Pigeon-Inspired Optimization [J]. Aerospace Science and Technology, 2018, 79: 518-530.
[8] LI H X, GAO F Y, HU C J, et al. Trajectory track for the landing of carrier aircraft with the forecast on the aircraft carrier deck motion [J]. Mathematical Problems in Engineering, 2021, 2021: 5597878.
[9] DUAN H B, CHEN L, ZENG Z G. Automatic landing for carrier-based aircraft under the conditions of deck motion and carrier airwake disturbances [J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(6): 5276-5291.
[10] CHEN C, TAN W Q, QU X J, et al. A fuzzy human pilot model of longitudinal control for a carrier landing task [J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 453-466.
[11] YUE L M, LIU G, HONG G X. Design and simulation of F/A-18A automatic carrier landing guidance controller [C]// AIAA Modeling and Simulation Technologies Conference. Washington: AIAA, 2016: AIAA2016-3527.
[12] YUAN S Z, YANG Y. The design of lateral automatic carrier landing system based on H-infinity control [J]. Ordnance Industry Automation, 2002, 21(6): 1-3 (in Chinese).
[13] ZHANG Y, WU W H, HU Y A, et al. Design of landing trajectory tracking robust controller for carrier-based unmanned aerial vehicle [J]. Control Theory & Applications, 2018, 35(4): 557-565 (in Chinese).
[14] LUNGU M H, CHEN M, V?ILCICˇA (DINU) D A. Backstepping- and sliding mode-based automatic carrier landing system with deck motion estimation and compensation [J]. Aerospace, 2022, 9(11): 644.
[15] LEE S, LEE J, LEE S, et al. Sliding mode guidance and control for UAV carrier landing [J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(2): 951-966.
[16] DUAN H B, YUAN Y, ZENG Z G. Automatic carrier landing system with fixed time control [J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 3586-3600.
[17] ZHANG Z, LI J T, ZOU S T, et al. Kinetic simulation research on aircraft landing and arresting [J]. Computer Simulation, 2015, 32(9): 75-79 (in Chinese).
[18] WANG L P, ZHANG Z, ZHU Q D, et al. Design of automatic carrier-landing controller based on compensating states and dynamic inversion [J]. IEEE Access, 2019, 7: 146939-146952.
[19] GUAN Z Y, MA Y P, ZHENG Z W, et al. Prescribed performance control for automatic carrier landing with disturbance [J]. Nonlinear Dynamics, 2018, 94(2): 1335-1349.
[20] GUAN Z Y, LIU H, ZHENG Z W, et al. Fixedtime control for automatic carrier landing with disturbance [J]. Aerospace Science and Technology, 2021, 108: 106403.
[21] BHATIA A K, JIANG J, ZHEN Z Y, et al. Robust adaptive preview control design for autonomous carrier landing of F/A-18 aircraft [J]. Aircraft Engineering and Aerospace Technology, 2021, 93(4): 642-650.
[22] ZHU Q D, YANG Z B. Dynamic recurrent fuzzy neural network-based adaptive sliding control for longitudinal automatic carrier landing system [J]. Journal of Intelligent & Fuzzy Systems, 2019, 37(1): 53-62.
[23] RICHALET J, RAULT A, TESTUD J L, et al. Model predictive heuristic control [J]. Automatica, 1978, 14(5): 413-428.
[24] KOO S, KIM S, SUK J, et al. Improvement of shipboard landing performance of fixed-wing UAV using model predictive control [J]. International Journal of Control, Automation and Systems, 2018, 16(6): 2697-2708.
[25] CUI K K, HAN W, SUN C, et al. Receding horizon longitudinal control technology for automatic carrier landing with variable reference trajectory based on sliding rate information [J]. IEEE Access, 2020, 8: 214742-214755.
[26] TANG K, WANG W, MENG Y E, et al. Flight control and airwake suppression algorithm for carrier landing based on model predictive control [J]. Transactions of the Institute of Measurement and Control, 2019, 41(8): 2205-2213.
[27] ZHOU Q X, WANG Y, SUN X A. UAV control based on gain adaptive super spiral sliding mode theory [J]. Journal of Shanghai Jiao Tong University, 2022, 56(11): 1453-1460 (in Chinese).
[28] DING M, MENG S, WANG S H, et al. Neuralnetwork-based adaptive feedback linearization control for 6-DOF wave compensation platform [J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 165-172(in Chinese).
[29] ZHEN Z Y, YU C J, JIANG S Y, et al. Adaptive super-twisting control for automatic carrier landing of aircraft [J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(2): 984-997.
[30] MENG Y E, WANG W, HAN H, et al. A visual/inertial integrated landing guidance method for UAV landing on the ship [J]. Aerospace Science and Technology, 2019, 85: 474-480.
[31] LIU K N, ZHAO W Y, SUN B G, et al. Application of updated Sage–Husa adaptive Kalman filter in the navigation of a translational sprinkler irrigation machine [J]. Water, 2019, 11(6): 1269.
[32] KIM K S, REW K H. Reduced order disturbance observer for discrete-time linear systems [J]. Automatica, 2013, 49(4): 968-975.
[33] GAN W Y, ZHU D Q, HU Z, et al. Model predictive adaptive constraint tracking control for underwater vehicles [J]. IEEE Transactions on Industrial Electronics, 2020, 67(9): 7829-7840.
[34] CHAKRABORTY A, SEILER P, BALAS G J. Susceptibility of F/A-18 flight controllers to the fallingleaf mode: Nonlinear analysis [J]. Journal of Guidance, Control, and Dynamics, 2011, 34(1): 73-85.