Model Predictive Control With Wind Preview for Aircraft Forced Landing

被引:8
作者
Fang, Xing [1 ]
Jiang, Jingjing [2 ]
Chen, Wen-Hua [2 ]
机构
[1] Jiangnan Univ, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China
[2] Loughborough Univ, Dept Aeronaut & Automot Engn, Loughborough LE11 3TU, England
基金
中国国家自然科学基金; 英国工程与自然科学研究理事会;
关键词
Aircraft; Aerospace control; Aircraft propulsion; Atmospheric modeling; Mathematical models; Aerospace electronics; Task analysis; Disturbance preview; economic model predictive control (EMPC); forced landing; unmanned aerial vehicles (UAVs); REACHABILITY; TIME; STABILITY;
D O I
10.1109/TAES.2023.3235321
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Autonomous emergency landing capability of fixed-wing aircraft is essential for opening airspace for civil unmanned aviation. This article proposes a goal-oriented control scheme to exploit wind information for the benefit of forced landing. Different from general disturbances in a classic control system, a favorable wind would help aircraft to glide to a selected landing site more easily so increase the level of safety while an adverse wind may render a selected landing site infeasible. We formulate the forced landing problem with wind preview information in the framework of economic model predictive control (EMPC), which aims to maximize the aircraft's final altitude when reaching a target region. A double-layer model predictive control (MPC) scheme is adopted to lessen the computational burden and to increase the prediction time window for practical implementation, where a piecewise-constant disturbance-preview-based EMPC maximizes the altitude at the upper level, and a linear MPC is employed at the lower level to track the reference signal optimized by the upper-level planner. Moreover, the effectiveness of the goal-oriented optimal control scheme is illustrated by several case studies, where an unmanned aircraft is gliding toward potential landing sites under various conditions.
引用
收藏
页码:3995 / 4004
页数:10
相关论文
共 35 条
  • [1] Adler A, 2012, CHIN CONT DECIS CONF, P2908, DOI 10.1109/CCDC.2012.6244461
  • [2] Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction
    Ak, Ronay
    Fink, Olga
    Zio, Enrico
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (08) : 1734 - 1747
  • [3] Reachability-Based Forced Landing System
    Akametalu, Anayo K.
    Tomlin, Claire J.
    Chen, Mo
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2018, 41 (12) : 2529 - 2542
  • [4] Economic optimization using model predictive control with a terminal cost
    Amrit, Rishi
    Rawlings, James B.
    Angeli, David
    [J]. ANNUAL REVIEWS IN CONTROL, 2011, 35 (02) : 178 - 186
  • [5] On Average Performance and Stability of Economic Model Predictive Control
    Angeli, David
    Amrit, Rishi
    Rawlings, James B.
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2012, 57 (07) : 1615 - 1626
  • [6] Beard R.W., 2012, SMALL UNMANNED AIRCR, DOI DOI 10.1515/9781400840601
  • [7] Disturbance-Observer-Based Control and Related Methods-An Overview
    Chen, Wen-Hua
    Yang, Jun
    Guo, Lei
    Li, Shihua
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2016, 63 (02) : 1083 - 1095
  • [8] Landing Site Reachability in a Forced Landing of Unmanned Aircraft in Wind
    Coombes, Matthew
    Chen, Wen-Hua
    Render, Peter
    [J]. JOURNAL OF AIRCRAFT, 2017, 54 (04): : 1415 - 1427
  • [9] Site Selection During Unmanned Aerial System Forced Landings Using Decision-Making Bayesian Networks
    Coombes, Matthew
    Chen, Wen-Hua
    Render, Peter
    [J]. JOURNAL OF AEROSPACE INFORMATION SYSTEMS, 2016, 13 (12): : 491 - 495
  • [10] Coombes M, 2015, INT CONF UNMAN AIRCR, P62, DOI 10.1109/ICUAS.2015.7152276