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Optimal aeroelastic vehicle sensor placement for root migration flight control applications.

机译：用于根部偏移飞行控制应用的最佳气动弹性车辆传感器放置。

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An important step in control design for elastic systems is the determination of the number and location of control system components, namely sensors. The number and placement of sensors can be critical to the robust functioning of active control systems, especially when the system of interest is a large high-speed aeroelastic vehicle. The position of the sensors affects not only system stability, but also the performance of the closed-loop system. In this dissertation, a new approach for sensor placement in the integrated rigid and vibrational control of flexible aircraft structures is developed. Traditional rigid-body augmentation objectives are addressed indirectly through input-output pair and compensation selection. Aeroelastic control suppression objectives are addressed directly through sensor placement. A nonlinear programming problem is posed to minimize a cost function with specified constraints, where the cost function terms are multiplied by appropriate weighting factors. Cost function criteria are based on complex frequency domain geometric pole-zero structures in order to gain stabilize or phase stabilize the aeroelastic modes. Specifically, these criteria are based on dipole magnitude and complementary departure angle. In turn, the control design approach utilizes one of the classical methods known as Evans root migration to exploit the pole-zero structures resulting from sensor placement. Desirable complementary departure angles can lead to significant aeroelastic damping improvement as loop gain is increased, while favorable dipole magnitudes can virtually eliminate the effects of aeroelastics in a feedback loop. Appropriate constraints include minimum phase aeroelastic zeros to avoid common problems associated with right-half plane zeros. To achieve desirable flight control system characteristics via optimal sensor locations, different kinds of blending filters for multiple sensors are investigated. Static filters, as well as dynamic filters with fixed or variable parameters and fixed or variable compensator parameters, are considered. For every cost function, there are several local minima indicating many distributions of the sensors are available. By evaluating the cost for each minimum, the global optimum can then be found.
机译：弹性系统控制设计中的重要步骤是确定控制系统组件（即传感器）的数量和位置。传感器的数量和位置对于主动控制系统的强大功能至关重要，特别是当目标系统是大型高速气动弹性车辆时。传感器的位置不仅影响系统稳定性，还影响闭环系统的性能。本文研究了一种在柔性飞机结构的刚度和振动集成控制中传感器放置的新方法。传统的刚体增强目标是通过输入输出对和补偿选择间接解决的。通过传感器放置可以直接解决气动弹性控制抑制目标。提出了非线性规划问题，以最小化具有指定约束的成本函数，其中成本函数项乘以适当的加权因子。成本函数准则基于复杂的频域几何零极点结构，以便获得稳定或相位稳定的气动弹性模态。具体来说，这些标准基于偶极子大小和互补离去角。反过来，控制设计方法利用一种称为Evans根迁移的经典方法来利用传感器放置产生的零极点结构。随着回路增益的增加，理想的互补偏离角可导致显着的气动弹性阻尼改善，而有利的偶极振幅实际上可消除反馈回路中气动弹性的影响。适当的约束条件包括最小相位气动弹性零点，以避免与右半平面零点相关的常见问题。为了通过最佳传感器位置获得理想的飞行控制系统特性，研究了用于多个传感器的不同种类的混合过滤器。考虑了静态滤波器以及具有固定或可变参数以及固定或可变补偿器参数的动态滤波器。对于每个成本函数，都有几个局部最小值，表示传感器的许多分布可用。通过评估每个最小值的成本，可以找到全局最优值。

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