关于大气飞行器的控制增益自适应调节方法研究

研究的目的是解决飞行器飞行空域和飞行状态大范围快速变化时姿态控制系统的适应性问题。采取的方法是首先通过某一大气飞行器的传递函数计算分析,找出对传递函数幅相特性影响比较大的关键动力学系数,再通过对关键动力学系数的全气动表变化规律研究,设计出姿态控制系统的动静态增益随M,α,β和q的变化规律,从而实现了动静态增益在整个飞行状态下的自适应调节,大大提高了姿态控制系统的裕度和适应性。     

一种非仿射高超声速飞行器姿态系统控制方法

针对非仿射高超声速飞行器姿态控制问题,提出了一种基于反步法的非线性控制方法。首先,通过干扰观测器估计攻角动态中的扰动量,在此基础上设计了俯仰角速度虚拟控制指令。然后,针对含有非仿射项的参数不确定的俯仰角速度动态函数,将其视为一个扩张状态,通过状态观测器对其进行估计。接着,基于动态逆方法设计了升降襟副翼的控制律并基于李雅普诺夫方法证明了闭环系统的稳定性。采用指令滤波器避免反步法应用中虚拟控制指令微分项的"复杂性爆炸"问题,并得到虚拟控制指令的一阶和二阶导数信号。所提方法能够适用于变速变高飞行模式。最后,通过对比仿真实验,验证了所设计控制方法的有效性。     

航天器零推进剂姿态机动启发式规划方法

针对零推进剂姿态机动约束耦合,导致规划效率低的问题,提出一种基于约束评价启发式的差分进化算法。基于零推进剂姿态机动约束分析,在规划空间中建立约束评价函数,对航天器约束和控制力矩陀螺约束进行解耦分类处理。设计基于约束评价函数的启发式策略,用于改进差分进化算法的种群变异,主动控制规划进程,引导零推进剂姿态机动规划序列满足多种约束,得到安全的姿态机动路径和控制力矩陀螺框架角路径。最后,数值仿真结果验证了该方法能够实现航天器零推进剂姿态机动规划。     

光纤惯导车辆姿态识别系统及其误差分析

光纤惯导具有分辨率高、动态范围大、灵敏度高、寿命长、自主性强、成本低等特点,将其应用到驾驶行为和车辆姿态识别系统中,通过对中低精度光纤惯导的车辆姿态识别系统存在的误差进行分析,提出车载惯导自适应快速对准模式、全参数自标定误差标校方法,以解决中低精度光纤惯导误差随时间不断累积的问题,实现车辆姿态识别自主性,为车辆姿态识别以及驾驶行为识别提供强有力支撑。分析和实验结果表明该车辆姿态识别系统可指导行车安全。     

Design and experimental study of a new flapping wing rotor micro aerial vehicle

A three-wing Flapping Wing Rotor Micro Aerial Vehicle (FWR-MAV) which can perform controlled flight is introduced and an experimental study on this vehicle is presented. A mechanically driven flapping rotary mechanism is designed to drive the three flapping wings and generate lift, and control mechanisms are designed to control the pose of the FWR-MAV. A flight control board for attitude control with robust onboard attitude estimation and a control algorithm is also developed to perform stable hovering flight and forward flight. A series of flight tests was conducted, with hovering flight and forward flight tests performed to optimize the control parameters and assess the performance of the FWR-MAV. The hovering flight test shows the ability of the FWR-MAV to counteract the moment generated by rotary motion and maintain the attitude of the FWR-MAV in space; the experiment of forward flight shows that the FWR-MAV can track the desired attitude.     

翱翔系列立方星的发展和展望

针对西北工业大学“翱翔”系列立方星在低成本、短周期、快响应的立方星技术方面的突破，介绍了应用于大气层外偏振导航技术试验的世界首颗12U立方星“翱翔之星”，世界首次组网开展低热层大气参数测量的QB50计划“翱翔一号”立方星等，总结了“翱翔”系列立方星的标准化结构设计、姿态控制系统、电源系统、星载计算机、通信与测控、部署器等核心系统的发展现状，介绍了自主研制的飞轮、磁力矩器、偏振敏感器载荷。并基于在轨组装，编队飞行，一箭多星等技术对“翱翔”系列立方星的应用进行了展望，对探索柔性、高精度、长寿命的立方星将有重要的意义。     

Positioning error compensation for parallel mechanism with two kinematic calibration methods

Compared with serial mechanisms, the parallel mechanism (PM) theoretically exhibited higher positioning accuracy, dynamic performance, strength-to-weight ratio, and lower manufacturing cost, but they had not been widely used in the practical application. One key issue, positioning accuracy, which directly affected their performance and was greatly influenced by the errors of kinematic structure parameters was analyzed. To effectively enhance the positioning precision of PMs, a novel modeless kinematic calibration method, namely the split calibration, was presented and its compensation effect of the positioning error was comprehensively compared with that of an integrated method on two different types of PMs. A strange phenomenon-correct and incorrect identified results were derived from two different PMs by the same integrated method, respectively-which had not been reported yet was discovered, and the origin of it was revealed utilizing numerical simulations. Finally, respective merits and drawbacks of these two methods obtained in this paper provided underlying insights to guide the practical application of the kinematic calibration for PMs.     

Lightweight hybrid visual-inertial odometry with closed-form zero velocity update

Visual-Inertial Odometry (VIO) fuses measurements from camera and Inertial Measurement Unit (IMU) to achieve accumulative performance that is better than using individual sensors. Hybrid VIO is an extended Kalman filter-based solution which augments features with long tracking length into the state vector of Multi-State Constraint Kalman Filter (MSCKF). In this paper, a novel hybrid VIO is proposed, which focuses on utilizing low-cost sensors while also considering both the computational efficiency and positioning precision. The proposed algorithm introduces several novel contributions. Firstly, by deducing an analytical error transition equation, one-dimensional inverse depth parametrization is utilized to parametrize the augmented feature state. This modification is shown to significantly improve the computational efficiency and numerical robustness, as a result achieving higher precision. Secondly, for better handling of the static scene, a novel closed-form Zero velocity UPdaTe (ZUPT) method is proposed. ZUPT is modeled as a measurement update for the filter rather than forbidding propagation roughly, which has the advantage of correcting the overall state through correlation in the filter covariance matrix. Furthermore, online spatial and temporal calibration is also incorporated. Experiments are conducted on both public dataset and real data. The results demonstrate the effectiveness of the proposed solution by showing that its performance is better than the baseline and the state-of-the-art algorithms in terms of both efficiency and precision. A related software is open-sourced to benefit the community.^①     

一种新型滑模控制算法在挠性多体卫星姿态控制中的应用

针对一类挠性多体卫星的复合控制问题，提出一种新型滑模变结构控制算法。新型算法利用闭环系统Lyapunov函数的一阶导数估计值设计控制器，且控制器采用了递归学习控制结构，能够有效解决传统滑模控制技术的颤振问题。随后根据Lyapunov稳定性理论证明闭环系统轨迹可以快速收敛到滑模面，并且系统状态误差可在滑模面上渐近收敛到零。此外，设计的控制器能够有效抑制外部干扰，而且控制器只需要控制输入矩阵信息而不需受控系统和未知参数的其他先验信息，使得算法具有较强鲁棒性。最后通过数值仿真与现有文献中控制算法进行对比，结果充分验证了本文设计控制算法的有效性和实用性。     

Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data.