对传感器失效具有完整性的神经网络控制器设计

本文研究了对一些传感器失效控制系统仍保持稳定性的问题，将多层前传神经网络用于这一问题，通过对神经网络的训练，使训练后的神经网络构成的控制器对传感器故障具有完整性。该方法对达到完整性设计的要求条件较低，且不受线性系统方法的限制。文末的仿真实例说明了所给方法的有效性。     

基于EFPI-FBG结构的光纤复合传感器研究

研究了一种基于非本征型Fabry-Perot干涉仪(EFPI)和光纤Bragg光栅(FBG)串联结构构成的EFPI-FBG光纤复合传感器,用于实现对压力和温度信号的同步测量。传感器选用熔融石英作为压力敏感材料,利用飞秒激光焊接技术实现传感器探头结构的封装。对传感器的工作原理进行了分析,制作并封装传感器工程样机,搭建测试系统,对传感器的性能进行了灵敏度及交叉敏感性分析。结果表明,该传感器在0.8 MPa范围内的灵敏度约为-0.729μm/MPa,在50℃~200℃范围的温度灵敏度为0.009 nm/℃,传感器压力测量最大允许误差为±4.12%FS。该EFPI-FBG复合传感器有望应用于压力—温度双参数测量需求的技术领域。     

面向自动驾驶仿真的虚拟LiDAR传感器建模

在自动驾驶仿真领域，虚拟传感器输出数据的精准度是仿真结果可靠性的重要保障。激光雷达（LiDAR）作为车辆环境感知的关键传感器，其采集的点云数据的准确性是实现车辆对三维环境理解的关键。但在虚拟环境中，通过3D渲染技术模拟的点云数据难以真实反映传感器在复杂工况下的变化规律。本文提出一种用于自动驾驶仿真的虚拟LiDAR传感器建模方法。该方法首先基于Unity 3D引擎构建LiDAR的几何测量模型。其次，结合真实传感器的衰变特性推导简化的LiDAR物理模型。最后，基于蒙特卡罗方法在随机模型上对仿真数据进行噪声模拟，从而实现高保真的LiDAR数据输出。所提出的方法可结合精细化的虚拟场景进行数据验证，实验结果表明：该方法能够有效地在虚拟环境下模拟LiDAR数据，从而应用于自动驾驶仿真算法验证过程。     

IABC: An iteratively automatic machine learning boosted hand-eye calibration method for laser displacement sensor

An on-machine measuring (OMM) system with a laser displacement sensor (LDS) is designed for measuring free-form surfaces of hypersonic aircraft’s radomes. To improve the measurement accuracy of the OMM system, a novel Iteratively Automatic machine learning Boosted hand-eye Calibration (IABC) method is proposed. Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC. Firstly, a new objective function is derived, containing analytical parameters of the hand-eye relationship and LDS errors. Then, a hybrid calibration model composed of two kernels is proposed to solve the objective function. One kernel is the analytical kernel designed for solving analytical parameters. Another kernel is the automatic machine learning (AutoML) kernel designed to model LDS errors. The two kernels are connected with stepwise iterations to find the best calibration results. Compared with traditional methods, hand-eye experiments show that IABC reduces the calibration RMSE by about 50%. Verification experiments show that IABC reduces the measurement deviations by about 25%-50% and RMSEs within 40%. Even when the training data are obviously less than the test data, IABC performs well. Experiments demonstrate that IABC is more accurate than traditional hand-eye methods.