论文标题
非线性模型识别和观察者设计，以估算小型涡轮喷气发动机的推力估算
Nonlinear Model Identification and Observer Design for Thrust Estimation of Small-scale Turbojet Engines
论文作者
论文摘要
喷射动力的垂直起飞和降落（VTOL）无人机需要精确的推力估计，以确保足够的稳定性边缘和稳健的操纵。小规模的涡轮喷气机已成为为重型空中无人机供电的好候选者。但是，由于这些涡轮喷气机中可用的仪器有限，因此使用经典技术估算精确的推力并不简单。在本文中，我们提出了一种方法，可以准确估计铁库中使用的小型涡轮机的在线推力 - 一种空中类人形机器人。我们使用灰色框方法根据从自定义发动机测试工作台获得的数据捕获具有非线性状态空间模型的涡轮喷气系统动力学。然后，该模型用于设计扩展的卡尔曼滤波器，该滤波器仅根据角速度测量值估算涡轮喷射的推力。我们利用了参数估计算法，以确保即使在发动机故障时，EKF也能提供平滑准确的估计值。在测试工作台上验证了设计的EKF，其中发现估计推力中的平均绝对误差在额定峰推力的2％以内。
Jet-powered vertical takeoff and landing (VTOL) drones require precise thrust estimation to ensure adequate stability margins and robust maneuvering. Small-scale turbojets have become good candidates for powering heavy aerial drones. However, due to limited instrumentation available in these turbojets, estimating the precise thrust using classical techniques is not straightforward. In this paper, we present a methodology to accurately estimate the online thrust for the small-scale turbojets used on the iRonCub - an aerial humanoid robot. We use a grey-box method to capture the turbojet system dynamics with a nonlinear state-space model based on the data acquired from a custom engine test bench. This model is then used to design an extended Kalman filter that estimates the turbojet thrust only from the angular speed measurements. We exploited the parameter estimation algorithm to ensure that the EKF gives smooth and accurate estimates even at engine failures. The designed EKF was validated on the test bench where the mean absolute error in estimated thrust was found to be within 2% of rated peak thrust.
