95毫克昆虫尺度扑翼飞行机器人的模型参考自适应飞行控制
Model-Reference Adaptive Flight Control of a 95-mg Insect-Scale Flapping-Wing Aerial Robot
AI总结：
针对昆虫尺度扑翼飞行机器人参数不确定性和扰动问题，提出模型参考自适应控制（MRAC）架构，结合混合乘性扩展卡尔曼滤波，实现高精度位置控制，并通过95毫克机器人实验验证了悬停和轨迹跟踪性能。
摘要：
由于系统尺度和复杂制造，描述扑翼昆虫尺度飞行机器人动力学的模型存在参数不确定性，例如惯性矩阵和飞行器的执行器映射。此外，由于其低惯性，这种机器人在飞行中受到随机和系统性扰动的严重影响，包括电源线张力、阵风和机翼不对中产生的非期望气动力。因此，在亚分克尺度上执行复杂机动的高性能要求机器人调整其行为以抵消扰动和模型不确定性。为此，我们引入了一种模型参考自适应控制（MRAC）架构，用于可实现为三维空间中刚体的扑翼机器昆虫的高性能位置控制。此外，我们展示了在飞行中实现混合乘性扩展卡尔曼滤波以估计当前和期望角速度，如何显著抑制姿态振动，特别是沿滚转和俯仰自由度，并提高飞行性能。为了展示所提方法的适用性、功能性和高性能，我们使用一个95毫克的昆虫尺度飞行机器人进行了实时悬停和轨迹跟踪六自由度飞行控制实验。
英文摘要：
Due to the system's scale and complex fabrication, the model describing the dynamics of a flapping-wing insect-scale aerial robot is subject to parameter uncertainty; for example, in the inertia matrix and the actuator mapping of the flier. Furthermore, due to its low inertia, this type of robot is greatly affected by stochastic and systematic disturbances during flight, including power-wire tension, gusts, and undesired aerodynamic forces produced by wing misalignment. Therefore, the high-performance execution of complex maneuvers at the subdecigram scale requires the robot to adapt its behavior to counteract disturbances and model uncertainty. Toward this objective, we introduce a model-reference adaptive control (MRAC) architecture for high-performance position control of flapping-wing robotic insects that can be modeled as rigid bodies in the three-dimensional (3D) space. In addition, we demonstrate how the implementation of a hybrid multiplicative extended Kálmán filter for estimating current and desired angular velocities during flight significantly dampens attitude vibrations, especially along the roll and pitch degrees of freedom (DOFs), and also improves flight performance. To show the suitability, functionality, and high performance of the proposed approach, we conducted real-time hovering and trajectory-tracking 6-DOF flight control experiments with a 95-mg insect-scale aerial robot.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
