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Unspecified final-time nonlinear suboptimal guidance of aerobatic aircraft in air race

机译:空气竞争中有氧飞机的未指明最终时间非线性指导

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This paper presents a new suboptimal nonlinear guidance design for aerobatic aircraft in air race. This guidance design needs to ensure several stringent terminal conditions at the specified race gates, and to satisfy various path inequality constraints dependent on state and input throughout the flight trajectory such as bounds on control, altitude, velocity as well as structural load factor limits. It also requires to be robust in presence of uncertainties in aerodynamic parameters as well as external disturbances due to wind gust, which makes this guidance design more challenging. To meet these demanding requirements, the guidance commands are generated using an innovative extension of the generalized model predictive static programming (G-MPSP) technique for incorporating both state and input inequality constraints in unspecified final time framework. This formulation readjusts final time depending on online performance of the system. In addition, to ensure various state and input inequality bounds throughout the trajectory, a penalty function approach is followed with this unspecified final time G-MPSP formulation. The effectiveness of this guidance design approach is validated in an air race scenario, while comparing with the Chebyshev pseudospectral method. Simulation results by considering a large number of simulations indicate that the proposed approach is successful under parameter uncertainties and disturbances, demonstrating its robustness aspect. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译:本文为空中竞争中的特技飞机提供了一种新的次优非线性指导设计。该指导设计需要确保指定的赛车门的几个严格的终端条件,并满足依赖于状态的各种路径不等式约束,并在整个飞行轨迹中输入,例如控制,高度,速度以及结构负载因子限制。它还需要在空气动力学参数中存在的不确定性以及由于风阵风引起的外部干扰,这使得这一指导设计更具挑战性。为了满足这些苛刻的要求,使用广义模型预测静态编程(G-MPSP)技术的创新扩展来生成指导命令,用于在未指定的最终时间框架中结合状态和输入不等式约束。此配方根据系统的在线性能,根据系统的在线表现重新调整最后一次。此外,为了确保在整个轨迹中的各种状态和输入不等式界限,遵循这种未指定的最终时间G-MPSP制定的惩罚功能方法。与Chebyshev Pseudtepectran方法相比,该指导设计方法的有效性在空中竞争方案中验证。通过考虑大量模拟的模拟结果表明所提出的方法是在参数不确定性和干扰下成功的,证明其鲁棒性方面。 (c)2021 Elsevier Masson SAS。版权所有。

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