Generation and Calibration of Linear Models of Aircraft with Highly Coupled Aeroelastic and Flight Dynamics

The lightweight structures and unconventional configurations being considered for the next generation of aircraft mean that any effort to predict or control the flight dynamics is impacted by the structural dynamics. One of the most severe forms of coupling between aeroelasticity and flight dynamics is an instability called body freedom flutter. The existing tools often assume a relatively weak effect of structural dynamics on the flight dynamics, and are therefore incapable of modeling strong interactions like body freedom flutter. A method of combining different sources of data traditionally used for aeroelasticity and flight dynamics is described by reconciling many of the differences between these models. By building upon past modeling efforts, a level of familiarity in the approach is achieved. Generally the differences from the traditional approaches are subtle but significant. The traditional frequency domain flutter model in a modal coordinate system is converted to a form consistent with a time domain flight dynamics model. The time domain rational function approximation about a non-inertial coordinate system and the unique constraints for the conversion between the inertial and non-inertial coordinate systems are discussed. A consistent transformation of the states of aeroelastic models to flight dynamics models is derived, which enables the integration of data from higher fidelity computational fluid dynamics models or wind-tunnel testing. The present method of integrating multidisciplinary data was used to create models that compare well with X-56A flight-test data, including conditions past the flutter speed

PAMPC: Perception-Aware Model Predictive Control for Quadrotors

We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sens- ing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, to- gether with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the contradiction between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions