基于逆动力学的柔性连杆机械臂的混合模糊控制

利用假设模态法建立了柔性连杆机械臂的逆动力学方程,提出了一种并行于常规开环逆动力学控制的混合模糊控制方法对其进行末端轨迹跟踪。设计的控制器由两部分组成,一部分为根据结构的逆动力学以及机械臂末端所需跟踪的轨迹计算出控制力;另一部分为通过末端位置的实测与给定轨迹的偏差,利用一个辅助模糊控制对控制量进行调整,构成输出反馈部分。通过数字仿真,并与常规模糊控制结果的比较,表明借助于结构逆动力学,可以降低模糊控制器对知识库的需求,获得较好的控制稳态性能。     

飞行器载低剖面倒F天线特性分析

用于飞行器上的遥测天线通常要求较宽的方向图覆盖范围、简单的外形结构和很低的天线高度。文中用CAD的方法计算并分析了飞行器上几种安装状态的低剖面倒 F形天线的辐射与输入驻波特性 ,提出一种改进型低剖面倒 F天线的设计思路     

A new grid deformation technology with high quality and robustness based on quaternion

Quality and robustness of grid deformation is of the most importance in the field of aircraft design, and grid in high quality is essential for improving the precision of numerical simulation. In order to maintain the orthogonality of deformed grid, the displacement of grid points is divided into rotational and translational parts in this paper, and inverse distance weighted interpolation is used to transfer the changing location from boundary grid to the spatial grid. Moreover, the deformation of rotational part is implemented in combination with the exponential space mapping that improves the certainty and stability of quaternion interpolation. Furthermore, the new grid deformation technique named ‘‘layering blend deformation' is built based on the basic quaternion technique, which combines the layering arithmetic with transfinite interpolation(TFI) technique. Then the proposed technique is applied in the movement of airfoil, parametric modeling, and the deformation of complex configuration, in which the robustness of grid quality is tested. The results show that the new method has the capacity to deal with the problems with large deformation, and the ‘‘layering blend deformation' improves the efficiency and quality of the basic quaternion deformation method significantly.     

用于表面温度测量的导热反问题的误差研究

讨论了用于温度测量的非稳态一维线性导热反问题。通过引入正则参数 ,这种不适定问题得到了稳定的解。在给出非级数形式的分析解后 ,计算误差可以通过数值解与准确解的比较确定。同时讨论了影响计算误差的各种因素。     

A variational Bayesian-based robust adaptive filtering for precise point positioning using undifferenced and uncombined observations

In the application of precise point positioning (PPP), especially in the dynamic mode, the classical Kalman filter (KF) usually produces a large number of estimation errors or diverges when there are gross errors in the observation data or unexpected turbulences occur in target motion state or both of them. For such problem, a variational Bayesian (VB)-based robust adaptive Kalman filtering (VB-RAKF) is proposed in this paper. This filter introduces a classification robust equivalent weight function to resist observation gross error and the inverse Wishart prior to model inaccurate process noise covariance matrix (PNCM). To improve the instantaneous accuracy of state estimation, the VB approach is used to obtain better estimations of inaccurate PNCM. Several sets of observation data collected by IGS reference stations and vehicles are employed to check the robustness and positioning accuracy of the VB-RAKF model. The results show that the VB-RAKF algorithm is more robust than the KF, and can effectively resist the gross error in observation data and control state disturbance. In the IGS reference station tests, when compared to the KF, the static positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 13%, 8% and 22%, respectively, and the simulated dynamic positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 19%, 9% and 21%, respectively. The in-vehicle dynamic test verifies that the VB-RAKF outperforms the KF, and shows that the VB-RAKF has better performance than the KF when dealing with observation data which has obvious gross errors, and similar performance as the KF when gross errors are small.     

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     

Start-up current adaptive control for sensorless high-speed brushless DC motors based on inverse system method and internal mode controller

The start-up current control of the high-speed brushless DC (HS-BLDC) motor is a chal-lenging research topic. To effectively control the start-up current of the sensorless HS-BLDC motor, an adaptive control method is proposed based on the adaptive neural network (ANN) inverse system and the two degrees of freedom (2-DOF) internal model controller (IMC). The HS-BLDC motor is identified by the online least squares support vector machine (OLS-SVM) algo-rithm to regulate the ANN inverse controller parameters in real time. A pseudo linear system is developed by introducing the constructed real-time inverse system into the original HS-BLDC motor system. Based on the characteristics of the pseudo linear system, an extra closed-loop feed-back control strategy based on the 2-DOF IMC is proposed to improve the transient response per-formance and enhance the stability of the control system. The simulation and experimental results show that the proposed control method is effective and perfect start-up current tracking perfor-mance is achieved.     

基于遗传算法的高超声速曲面压缩进气道反设计

结合计算流体力学和遗传算法,建立了一种高超声速曲面压缩进气道的反设计方法。根据压力分布反设计了压缩型面。结果表明,该曲面压力分布与目标压力分布符合良好,从而验证了反设计方法的正确性。采用此反设计方法,设计了某高超声速曲面压缩进气道,并和等熵压缩二维进气道进行了比较。研究发现,在其它性能参数几乎相等情况下,曲面压缩进气道总压恢复较等熵压缩基准进气道提高9.7%,长度缩短5.6%。吞入23mm前体附面层后,基准进气道不起动,而曲面压缩进气道总压恢复系数仅下降5.4%,表现出良好的吞附面层能力。     

Damage assessment of large space structures through the variational approach

The present investigation is focused on the solution of a dynamic inverse problem which is concerned with the assessment of damage in large space structures by means of measured vibration data. This inverse problem has been presented as an optimization problem and has been solved through the use of the conjugate gradient method with the adjoint equation, also called the variational approach. When a high number of damaged elements has to be found and these elements are also severely damaged, it is shown that the use of an additional method is necessary in order to provide a better initial guess for the conjugate gradient method. A stochastic method, represented by the genetic algorithm method, has been chosen because it provides robust search in complex spaces and also reduces the chance of converging to local optima. The application of this hybrid approach showed that better results can be achieved, although the computational time for the application analyzed here could increase. The damage estimation has been evaluated using noiseless and noisy synthetic experimental data, and the reported results are concerned with a space truss structure.