关于多目标气动优化设计中伴随方法与对策理论的耦合研究(英文)

本文研究了多目标气动优化设计中伴随方法和对策理论的耦合问题,给出了伴随方法在每个对策策略中的具体应用和数值方法。在合作对策中伴随方法被并行地执行以共同得到Pareto阵面上的最优解;相反在Nash对策中伴随方法在Nash策略的最后被耦合起来以互相竞争的形式得到各自目标的最优解;而在Stackel-berg对策中伴随方法被分层耦合进"领头人"和"跟随者"的策略中以求解他们相互依赖的平衡解。文末的优化算例表明了本文耦合算法及其数值过程在求解多目标气动优化设计问题中的有效性。     

一种新的微细精密航空制造技术—纯水微细电解加工的工艺研究(英文)

为了寻求航空精密微细制造新技术并实现绿色制造和精密微细制造,对一种新型微细电解加工方法——纯水微细电解加工进行了研究。基于水解离机理,在新研制的试验装置上,采用不同的试验条件,进行了一系列工艺试验,探索并揭示了实现纯水微细电解加工的必要工艺条件和工艺规律,加工了圆孔和字母"PW-ECM";还研究了超声振动—纯水电解复合加工新方法,进而在不锈钢薄片上加工出三角、方形盲孔、三角通孔;试验研究结果证明了纯水微细电解加工的可行性。     

Modeling and Optimal Design of 3 Degrees of Freedom Helmholtz Resonator in Hydraulic System

Three degrees of freedom (3-DOF) Helmholtz resonator which consists of three cylindrical necks and cavities connected in series (neck-cavity-neck-cavity-neck-cavity) is suitable to reduce flow pulsation in hydraulic system. A novel lumped parameter model (LPM) of 3-DOF Helmholtz resonator in hydraulic system is developed which considers the viscous friction loss of hydraulic fluid in the necks. Applying the Newton’s second law of motion to the equivalent mechanical model of the resonator, closed-form expression of transmission loss and resonance frequency is presented. Based on the LPM, an optimal design method which employs rotate vector optimization method (RVOM) is proposed. The purpose of the optimal design is to search the resonator’s unknown parameters so that its resonance frequencies can coincide with the pump-induced flow pulsation harmonics respectively. The optimal design method is realized to design 3-DOF Helmholtz resonator for a certain type of aviation piston pump hydraulic system. The optimization result shows the feasibility of this method, and the simulation under optimum parameters reveals that the LPM can get the same precision as transfer matrix method (TMM).     

Aerodynamic Modeling and Parameter Estimation from QAR Data of an Airplane Approaching a High-altitude Airport

Aerodynamic modeling and parameter estimation from quick accesses recorder (QAR) data is an important technical way to analyze the effects of highland weather conditions upon aerodynamic characteristics of airplane. It is also an essential content of flight accident analysis. The related techniques are developed in the present paper, including the geometric method for angle of attack and sideslip angle estimation, the extended Kalman filter associated with modified Bryson-Frazier smoother (EKF-MBF) method for aerodynamic coefficient identification, the radial basis function (RBF) neural network method for aerodynamic modeling, and the Delta method for stability/control derivative estimation. As an application example, the QAR data of a civil airplane approaching a high-altitude airport are processed and the aerodynamic coefficient and derivative estimates are obtained. The estimation results are reasonable, which shows that the developed techniques are feasible. The causes for the distribution of aerodynamic derivative estimates are analyzed. Accordingly, several measures to improve estimation accuracy are put forward.     

A Dual-driven Intelligent Combination Control of Heat Pipe Space Cooling System

Effective thermal control systems are essential for reliable operation of spacecraft.A dual-driven intelligent combination control strategy is proposed to improve the temperate control and heat flux tracking effects.Both temperature regulation and heat flux tracking errors are employed to generate the final control action;their contributions are adaptively adjusted by a fuzzy fusing policy of control actions.To evaluate the control effects,describe a four-nodal mathematical model for analyzing the dynamic characteristics of the controlled heat pipe space cooling system(HP-SCS) consisting of an aluminum-ammonia heat pipe and a variable-emittance micro-electromechanical-system(MEMS) radiator.This dynamical model calculates the mass flow-rate and condensing pressure of the heat pipe working fluid directly from the systemic nodal temperatures,therefore,it is more suitable for control engineering applications.The closed-loop transient performances of four different control schemes have been numerically investigated.The results conclude that the proposed intelligent combination control scheme not only improves the thermal control effects but also benefits the safe operation of HP-SCS.     

Robust Hybrid Control for Ballistic Missile Longitudinal Autopilot

This paper investigates the boost phase’s longitudinal autopilot of a ballistic missile equipped with thrust vector control. The existing longitudinal autopilot employs time-invariant passive resistor-inductor-capacitor (RLC) network compensator as a control strategy, which does not take into account the time-varying missile dynamics. This may cause the closed-loop system instability in the presence of large disturbance and dynamics uncertainty. Therefore, the existing controller should be redesigned to achieve more stable vehicle response. In this paper, based on gain-scheduling adaptive control strategy, two different types of optimal controllers are proposed. The first controller is gain-scheduled optimal tuning-proportional-integral-derivative (PID) with actuator constraints, which supplies better response but requires a priori knowledge of the system dynamics. Moreover, the controller has oscillatory response in the presence of dynamic uncertainty. Taking this into account, gain-scheduled optimal linear quadratic (LQ) in conjunction with optimal tuning-compensator offers the greatest scope for controller improvement in the presence of dynamic uncertainty and large disturbance. The latter controller is tested through various scenarios for the validated nonlinear dynamic flight model of the real ballistic missile system with autopilot exposed to external disturbances.     

Control of a Spherical Robot: Path Following Based on Nonholonomic Kinematics and Dynamics

This paper presents the controller design for the path following of a spherical mobile robot,BHQ-1.Firstly,a desired velocity for the reference path is deduced from the kinematic model,which cannot be transformed into the classic chained form.Secondly,a necessary torque for the desired velocity is obtained based on the dynamic model.As to the kinematics,a one-dimensional function is selected to measure the two-directional tracking error,and the velocity of rolling forward is reasonably assumed to be constant;therefore the multiple-input multiple-output (MIMO) system is transformed into a single-input single-output (SISO) system.As to the dynamics,both exact dynamics and inexact dynamics with modeling error as well as bounded unknown disturbance are taken into account,based on which a proportional-derivative (PD) controller and a sliding mode controller with adaptive parameters are proposed respectively.Finally,convergence analysis and simulation results are provided to validate these controllers.     

Image Smearing Modeling and Verification for Strapdown Star Sensor

To further extend study on celestial attitude determination with strapdown star sensor from static into dynamic field, one prerequisite is to generate precise dynamic simulating star maps. First a neat analytical solution of the smearing trajectory caused by spacecraft attitude maneuver is deduced successfully, whose parameters cover the geometric size of optics, three-axis angular velocities and CCD integral time. Then for the first time the mathematical law and method are discovered about how to synthesize the two formulae of smearing trajectory and the static Gaussian distribution function (GDF) model, the key of which is a line integral with regard to the static GDF attenuated by a factor 1/ L s (L s is the arc length of the smearing trajectory) along the smearing trajectory. The dynamic smearing model is then obtained, also in an analytical form. After that, three sets of typical simulating maps and data are simulated from this dynamic model manifesting the expected smearing effects, also compatible with the linear model as its special case of no boresight rotation. Finally, model validity tests on a rate turntable are carried out, which results in a mean correlation coefficient 0.920 0 between the camera images and the corresponding model simulated ones with the same parameters. The sufficient similarity verifies the validity of the dynamic smearing model. This model, after parameter calibration, can serve as a front-end loop of the ground semi-physical simulation system for celestial attitude determination with strapdown star sensor.     

Asymptotic Performance of Sparse Signal Detection Using Convex Programming Method

The detection of sparse signals against background noise is considered. Detecting signals of such kind is difficult since only a small portion of the signal carries information. Prior knowledge is usually assumed to ease detection. In this paper, we consider the general unknown and arbitrary sparse signal detection problem when no prior knowledge is available. Under a Neyman-Pearson hypothesis-testing framework, a new detection scheme is proposed by combining a generalized likelihood ratio test (GLRT)-like test statistic and convex programming methods which directly exploit sparsity in an underdetermined system of linear equations. We characterize large sample behavior of the proposed method by analyzing its asymptotic performance. Specifically, we give the condition for the Chernoff-consistent detection which shows that the proposed method is very sensitive to the 2 norm energy of the sparse signals. Both the false alarm rate and the miss rate tend to zero at vanishing signal-to-noise ratio (SNR), as long as the signal energy grows at least logarithmically with the problem dimension. Next we give a large deviation analysis to characterize the error exponent for the Neyman-Pearson detection. We derive the oracle error exponent assuming signal knowledge. Then we explicitly derive the error exponent of the proposed scheme and compare it with the oracle exponent. We complement our study with numerical experiments, showing that the proposed method performs in the vicinity of the likelihood ratio test (LRT) method in the finite sample scenario and the error probability degrades exponentially with the number of observations.     

Defect Recognition in Thermosonic Imaging

This work is aimed at developing an effective method for defect recognition in thermosonic imaging.The heat mechanism of thermosonic imaging is introduced,and the problem for defect recognition is discussed.For this purpose,defect existing in the inner wall of a metal pipeline specimen and defects embedded in a carbon fiber reinforced plastic(CFRP) laminate are tested.The experimental data are processed by pulse phase thermography(PPT) method to show the phase images at different frequencies,and the characteristic of phase angle vs frequency curve of thermal anomalies and sound area is analyzed.A binary image,which is based on the characteristic value of defects,is obtained by a new recognition algorithm to show the defects.Results demonstrate good defect recognition performance for thermosonic imaging,and the reliability of this technique can be improved by the method.