The Special Issue on aircraft systems integration and control is a series of selected papers from the European Conference for Aerospace Sciences (EUCASS) held at Politecnico di Milano in Milan, Italy, on 3–6 July 2017. This was the 7th conference organized by EUCASS. Previous conferences were held in Moscow (2005), Brussels (2007), Versailles (2009), St. Petersburg (2011), Munich (2013) and Krakow (2015). The most recent EUCASS conference was held in Madrid in 2019. The EUCASS 2017 conference attracted over 500 papers in five symposia including System Integration; Flight Dynamics, Guidance, Navigation and Control; Flight Physics; Propulsion Physics; Structures and Materials. A total of 12 papers were selected out of 54 papers presented in nine related sessions in the Flight Dynamics/GNC and System Integration symposia. The papers were subject to external review and revision by the authors in accordance with the policy of the Journal. These papers constitute the present Special Issue on aircraft systems integration and control. A brief summary is presented below. The first five papers are from the realm of aircraft control, with the first three papers innovating in attitude control theory and application. The paper ‘‘Adaptive augmentation of the attitude control system for a multirotor unmanned aerial vehicle,’’ by G. Bressan et al. introduces a methodology that improves the disturbance rejection performances of a given baseline controller via observer-based model reference adaptive control. Not only does the proposed controller offer robustness with respect to parameter uncertainty, but it can also mitigate a loss-ofthrust anomaly. The theory is brilliantly validated by testing results on a quadrotor UAV and performs favorably compared with state-of-the-art techniques like L1 control. In ‘‘Reconfiguration control method for non-redundant actuator faults on unmanned aerial vehicle’’ by A. Boche, J.-L. Farges, and H. De Plinval, the authors tackle the issues of engine and elevon failures affecting the longitudinal dynamics of a fixed wing UAV. For this purpose, they take the path of indirect adaptive control. Mastering the theory and art of jump-linear quadratic control, ofmodel predictive control, and of multiple model adaptive estimation, the authors design a sound reconfiguration control method and verify it via an in-house high-fidelity UAV simulator. As opposed to the first two papers, where changes in the actuators are failures to be compensated, the third paper, ‘‘Neural network-based nonlinear dynamic inversion control of variable – spanmorphing aircraft’’, by J. Lee andY.Kim, seeks those changes in the aircraft plant parameters, here through geometry morphing, that foster aggressive maneuvers in a typical highalpha jet fighter, e.g. push-over, pull-up, high g-maneuvers. The authors harvest sophisticated techniques in direct adaptive control, apply neural network identification of the aircraft attitude inverse dynamics, and carefully design the adaptation laws while fulfilling flying qualities requirements. Henceforth, they lay down an efficient framework for future morphing optimization problems. ‘‘Comparison of a closed-loop control by means of high-fidelity and low-fidelity coupled CFD/RBD computations’’ byM. Franze focuses on the dilemma that every control practitioner faces in realistic controller developments: to choose a design model that is simple, fostering the ease and shortness of a development cycle, or one that is sophisticated, expecting better performances in realistic verification and validation tests. In his in-depth and insightful study dedicated to the closed-loop flight-path control design for a six degrees-of-freedom sounding rocket, M. Franze successfully guides us through the opportunities and pitfalls of combining low-fi and high-fi models of challenging environments where rigid body dynamics and atmospheric flow dynamics are tightly coupled. To conclude the aircraft control part of this Special Issue, the fifth paper brings a novel contribution to the realm of air traffic control, a worldwide challenge in the context of ever-growing air traffic density. In ‘‘Optimal scheduling algorithm in point merge system including holding pattern based onmixed-integer linear programming’’, by S. Lee, Y. Hong and Y. Kim, the authors focus on traffic integration in the vicinity of large civil airports, with an Proc IMechE Part G: J Aerospace Engineering 2020, Vol. 234(10) 1585–1586 ! IMechE 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0954410020944631 journals.sagepub.com/home/pig

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飞机系统集成与控制专刊介绍

关于飞机系统集成和控制的特刊是 2017 年 7 月 3 日至 6 日在意大利米兰理工大学举行的欧洲航空航天科学会议 (EUCASS) 的一系列精选论文。这是 EUCASS 组织的第 7 次会议. 之前的会议分别在莫斯科（2005 年）、布鲁塞尔（2007 年）、凡尔赛宫（2009 年）、圣彼得堡（2011 年）、慕尼黑（2013 年）和克拉科夫（2015 年）举行。最近的 EUCASS 会议于 2019 年在马德里举行。 EUCASS 2017 会议在包括系统集成在内的五个专题讨论会上吸引了 500 多篇论文；飞行动力学、制导、导航和控制；飞行物理学；推进物理学；结构和材料。在飞行动力学/GNC 和系统集成研讨会的九个相关会议上发表的 54 篇论文中，共有 12 篇论文被选中。论文由作者根据期刊的政策进行外部审查和修订。这些论文构成了当前关于飞机系统集成和控制的特刊。简要总结如下。前五篇论文来自飞机控制领域，前三篇是姿态控制理论与应用的创新。G. Bressan 等人的论文“多旋翼无人机姿态控制系统的自适应增强”。介绍了一种方法，该方法通过基于观测器的模型参考自适应控制来提高给定基线控制器的抗扰性能。所提出的控制器不仅在参数不确定性方面提供了鲁棒性，而且还可以减轻推力损失异常。该理论通过在四旋翼无人机上的测试结果得到了出色的验证，并且与 L1 控制等最先进的技术相比表现出色。在 A. Boche, J.-L. 的“无人机非冗余执行器故障的重构控制方法”中。Farges 和 H. De Plinval，作者解决了影响固定翼无人机纵向动力学的发动机和升降舵故障问题。为此，他们采取了间接自适应控制的路径。作者掌握了跳跃线性二次控制、模型预测控制和多模型自适应估计的理论和技术，设计了一种完善的重构控制方法，并通过内部高保真无人机模拟器进行验证。与前两篇论文相反，在第三篇论文中，执行器的变化是无法得到补偿的，“基于神经网络的可变跨度飞机非线性动态反演控制”，作者 J. Lee 和 Y.Kim，通过几何变形来寻求飞机工厂参数的那些变化，这在典型的高阿尔法喷气式战斗机中促进了攻击性机动，例如俯卧撑、引体向上、高重力动作。作者在直接自适应控制中收获了复杂的技术，应用了飞机姿态逆动力学的神经网络识别，并在满足飞行质量要求的同时仔细设计了自适应律。此后，他们为未来的变形优化问题制定了一个有效的框架。''通过高保真和低保真耦合 CFD/RBD 计算的闭环控制的比较'' by M。Franze 专注于每个控制从业者在现实控制器开发中面临的困境：选择简单的设计模型，促进开发周期的轻松和缩短，或者选择复杂的设计模型，期望在现实的验证和验证测试中获得更好的性能。在致力于六自由度探空火箭的闭环飞行路径控制设计的深入而有见地的研究中，M. Franze 成功地引导我们克服了结合低保真和高保真的机会和陷阱具有挑战性的环境模型，其中刚体动力学和大气流动动力学紧密耦合。作为本特刊的飞机控制部分的总结，第五篇论文为空中交通管制领域带来了新的贡献，在空中交通密度不断增长的背景下，这是一项全球性的挑战。在 S. Lee、Y. Hong 和 Y. Kim 的“基于混合整数线性规划的包含等待模式的点合并系统中的最优调度算法”中，作者专注于大型民用机场附近的交通整合， Proc IMechE Part G：J Aerospace Engineering 2020，Vol。234(10) 1585–1586 ！IMechE 2020 文章重用指南：sagepub.com/journals-permissions DOI：10.1177/0954410020944631 journals.sagepub.com/home/pig 234(10) 1585–1586 ！IMechE 2020 文章重用指南：sagepub.com/journals-permissions DOI：10.1177/0954410020944631 journals.sagepub.com/home/pig 234(10) 1585–1586 ！IMechE 2020 文章重用指南：sagepub.com/journals-permissions DOI：10.1177/0954410020944631 journals.sagepub.com/home/pig