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Trajectory Optimization to Precisely Position Models in High-Fidelity Simulation

机译:高保真仿真中精确定位模型的轨迹优化

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An approach for solving aggressive user-specified trajectories in high-fidelity simulations is presented. The objective is to solve for control input histories that satisfy user-defined vehicle states at specified way points in time. In this framework, the user can specify the full rigid-body state of the vehicle, which includes position, velocity, attitude angles, and angular rates. This allows a simulation-user, without expert piloting skills, to push a vehicle model to the limits of its flight envelope, yet in an intuitive and repeatable fashion. These objectives can be accomplished in a variety of ways, including direct and indirect methods for trajectory optimization. This paper presents a direct method employing a gradient-based optimization algorithm within a "direct multiple shooting" formulation. A highly unstable ducted fan rotorcraft aerodynamic model in a 6-degree-of-freedom simulation is the basis for optimizations. Comparisons between optimization of the open-loop aircraft and a rate-stabilized aircraft model are given, showing that unstable models may experience problems with convergence. Using a rate-stabilizing controller in conjunction with the high-fidelity rotorcraft model greatly improved the optimization convergence success rate. The method is demonstrated for several maneuvers of varying complexity.
机译:提出了一种在高保真仿真中解决用户指定的激进轨迹的方法。目的是解决在指定的航路时间点满足用户定义的车辆状态的控制输入历史记录。在此框架中,用户可以指定车辆的完整刚体状态,其中包括位置,速度,姿态角和角速度。这允许模拟用户在没有专家驾驶技能的情况下,以直观且可重复的方式将飞行器模型推向其飞行包线的极限。这些目标可以通过多种方式实现,包括直接和间接的轨迹优化方法。本文提出了一种在“直接多次射击”公式中采用基于梯度的优化算法的直接方法。 6自由度模拟中的高度不稳定的风管旋翼航空器空气动力学模型是进行优化的基础。给出了开环飞机优化和速率稳定飞机模型之间的比较,表明不稳定模型可能会遇到收敛问题。结合使用速率稳定控制器和高保真旋翼飞机模型,可以极大地提高优化收敛的成功率。演示了该方法可用于多种复杂程度不同的操作。

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