高超声速飞行器自适应反步控制器设计

针对含有不确定干扰项的吸气式高超声速飞行器模型,分别设计了基于反馈线性化的速度控制器和基于反步法的航迹控制器,以实现对速度和航迹角参考信号的稳定跟踪。通过指令滤波器得到俯仰角的实际跟踪指令及其一阶、二阶微分信号,可直接设计升降舵控制指令,在解决了虚拟控制量求导"复杂性爆炸"问题的同时,减少了反推计算步数,从而达到提高系统动态性能和优化控制器结构的目的。基于LaSalle不变集原理和Lyapunov理论设计的自适应更新律保证了系统的稳定性。仿真结果表明,所设计的控制器在飞行器存在不确定干扰的情况下仍能满足对参考信号跟踪性能的要求。     

Filtering of moving targets using SBIR sequential frames

In this paper three-dimensional (3-D) finite-impulse response (FIR) filters are proposed for moving target detection and tracking from multiframe space-based infrared (SBIR) data. An optimal, in the l_p sense, 3-D FIR filter design technique is proposed which is suitable for the above application. This technique is the first 3-D FIR design of its kind presented in the open literature. Directional, matched, and adaptive 3-D filtering techniques are proposed. Prior to the filtering, clutter mean estimation and mean subtraction are required. Real time implementation of directional and/or matched filters for processing maneuvering targets is discussed and filter design methods are proposed. Finally, performance comparisons of the proposed and other available 3-D FIR and infinite-impulse response (IIR) filters, on real SBIR data, are presented in which the advantages of the proposed 3-D filters are shown     

基于BOWA小脑模型的高精度稳定 电动加载系统

 随着电动加载系统的不断发展,对控制精度、动态特性和稳定性提出了更高的要求,常规的小脑模型(CMAC)和PD控制相结合的复合控制策略难以满足加载指标要求。针对无人机舵机电动加载系统的控制需求,提出了一种基于平衡学习、最优权值和自适应学习率的新型小脑模型(BOWA-CMAC)复合控制策略,它在保留小脑模型算法正常学习过程的同时,避免了算法的过学习现象,保证了系统的稳定,同时提高了跟踪精度和动态特性。仿真和实验结果表明,BOWA-CMAC复合控制策略具有很强的鲁棒性,抑制了加载系统的多余力矩,保证了系统的稳定性,有效提高了系统的跟踪精度和动态特性,非常适合于实时控制。     

挠性航天器的退步直接自适应姿态跟踪控制

针对参数不确定的挠性航天器姿态跟踪控制问题,提出了一种退步直接自适应控制算法。首先验证了挠性航天器动力学子系统的近似严格正实性,并设计了具有理想控制性能的参考模型;然后对以姿态四元数描述的运动学子系统设计常系数输出反馈中间控制律,使航天器姿态四元数输出渐近跟踪参考模型输出;最后退一步,对具有参数不确定特性的动力学子系统,基于非线性直接自适应控制理论和Lyapunov稳定性理论,设计了退步直接自适应姿态跟踪控制器,并证明了闭环系统的稳定性。仿真结果表明,所提控制方法能有效抑制挠性附件的振动,对挠性航天器的控制是有效的。     

A Nonlinear Tracker Using Attitude Measurements

The subject of this paper involves tracking the present position of a maneuvering aircraft as well as predicting its future position. A tracking filter is developed that uses aircraft attitude angles (yaw, pitch, roll) in addition to the usual radar measurements. Computer simulation of tracker performance when tracking violently maneuvering aircraft indicates that a dramatic improvement is obtained by using attitude information. The approach taken is to develop a 12-or 15-state extended Kalman filter that models both translational and rotational degrees of freedom. By measuring and estimating attitude it is possible to approximately determine the magnitude and direction of the force system acting on the vehicle and therefore determine vehicle linear acceleration. Knowledge of acceleration is then used to improve the estimate of present and future position of the vehicle being tracked. Simulation of a T-38 aircraft performing a 5 g turn indicates that the new tracker produces maximum trajectory prediction errors that are 36 percent of the errors experienced by more conventional trackers.     

带时变通信时间延迟的卫星编队姿态协同自适应L2增益控制

针对卫星编队姿态协同分布式控制问题,提出一种基于Lyapunov方法的编队飞行协同控制策略.首先,考虑到实际编队飞行中星问通信存在时变时间延迟及模型不确定问题,结合变结构控制的思想设计一种针对时滞系统稳定性分析的Lyapunov函数,从而由直接Lyapunov方法得到可对模型参数进行估计的自适应分布式姿态协同控制器,并...     

自适应高阶容积卡尔曼滤波在目标跟踪中的应用

针对传统容积卡尔曼滤波(CKF)在系统状态发生突变时估计精度下降的问题,将强跟踪滤波(STF)算法与高阶容积卡尔曼滤波(HCKF)算法相结合,提出了一种自适应高阶容积卡尔曼滤波(AHCKF)方法。该算法采用高阶球面-相径容积规则,可获得高于传统CKF的估计精度,同时在HCKF算法中引入STF,通过渐消因子在线修正预测误差协方差阵,强迫残差序列正交,提高了算法的鲁棒性,增强了算法应对系统状态突变等不确定因素的能力。将提出的AHCKF算法应用于具有状态突变的机动目标跟踪问题并进行数值仿真,仿真结果表明,AHCKF算法在系统状态发生突变的情况下表现出良好的滤波性能,有效地避免了状态突变造成的滤波精度下降,较传统的CKF、HCKF、交互式多模型-容积滤波(IMM-CKF)和自适应容积卡尔曼滤波(ACKF)算法有更强的鲁棒性和系统自适应能力。     

带有执行器约束条件的模糊自适应反步控制

针对高超声速飞行器在飞行控制过程中存在外界干扰以及考虑执行器的动态特性等问题,结合飞行器纵向模型的特点,考虑舵的动态特性,分别设计了基于动态逆的速度控制器和基于指令滤波器采用Backstepping控制方法的高度控制器。模糊自适应系统用来在线辨识飞行器模型由于气动参数的变化而引起的不确定性。运用Lyapunov理论分析闭环系统的稳定性,证明了包含跟踪误差在内的所有信号满足半全局一致稳定。最后通过仿真对控制器的控制效果进行验证,得到了较为满意的控制效果。     

Navigation system design using time-varying complementary filters

A new methodology for the design of navigation systems for autonomous vehicles is introduced. Using simple kinematic relationships, the problem of estimating the velocity and position of an autonomous vehicle is solved by resorting to special bilinear time-varying filters. These are the natural generalization of linear time-invariant complementary filters that are commonly used to properly merge sensor information available at low frequency with that available in the complementary region. Complementary filters lend themselves to frequency domain interpretations that provide valuable insight into the filtering design process. This work extends these properties to the time-varying setting by resorting to the theory of linear differential inclusions and by converting the problem of weighted filter performance analysis into that of determining the feasibility of a related set of linear matrix inequalities (LMIs). Using this set-up, the stability of the resulting filters as well as their "frequency-like" performance can be assessed using efficient numerical analysis tools that borrow from convex optimization techniques. The mathematical background that is required for complementary time-varying filter analysis and design is introduced. Its application to the design of a navigation system that estimates position and velocity of an autonomous vehicle by complementing position information available from GPS with the velocity information provided by a Doppler sonar system is described.     

压电陶瓷叠层作动器迟滞蠕变非线性自适应混合补偿控制方法

压电陶瓷叠层作动器的迟滞蠕变非线性特性严重影响了控制系统的稳定性及动态跟踪精度。针对其迟滞蠕变非线性补偿控制问题,提出了一种高精度动态补偿压电陶瓷叠层作动器非线性特性的自适应混合补偿控制方法,即迟滞蠕变前馈补偿与自适应滤波反馈补偿结合的前馈-反馈混合控制方法。采用改进的Prandtl-Ishlinskii（Modified Prandtl-Ishlinskii,MPI）模型对压电陶瓷叠层作动器迟滞蠕变非线性特性进行精细化建模,并得到其逆补偿模型进行前馈补偿。根据前馈补偿误差,采用自适应滤波反馈控制对输入信号进行实时调控,实现对压电陶瓷叠层作动器的迟滞非线性及lg（t）型蠕变特性的实时精确补偿控制。数值仿真与实验结果表明,相比于常规前馈迟滞蠕变补偿,所提出的自适应混合补偿控制方法可以有效降低压电陶瓷叠层作动器的迟滞补偿误差,极大提高了迟滞蠕变非线性动态跟踪精度以及自适应性。     

Waveform selective probabilistic data association

An adaptive, waveform selective probabilistic data association (WSPDA) algorithm for tracking a single target in clutter is presented. The assumption of an optimal receiver allows the inclusion of transmitted waveform specification parameters in the tracking subsystem equations, leading to a waveform selection scheme where the next transmitted waveform parameters are selected so as to minimize the average total mean-square tracking error at the next time step. Semiclosed form solutions are given to the local (one-step-ahead) adaptive waveform selection problem for the case of one-dimensional target motion. A simple simulation example is given to compare the performance of a tracking system using a WSFDA based tracking filter with that of a conventional system with a fixed waveform shape and probabilistic data association (PDA) tracking filter.     

Extended solution to multiple maneuvering target tracking

An improved algorithm for tracking multiple maneuvering targets is presented. This approach is implemented with an approximate adaptive filter consisting of the one-step conditional maximum-likelihood technique together with the extended Kalman filter and an adaptive maneuvering compensator. In order to avoid the extra computational burden of considering events with negligible probability, a validation matrix is defined in the tracking structure. With this approach, data-association and target maneuvering problems can be solved simultaneously. Detailed Monte Carlo simulations of the algorithm for many tracking situations are described. Computer simulation results indicate that this approach successfully tracks multiple maneuvering targets over a wide range of conditions     

一种针对结构损伤的非线性容错飞行控制方法

飞机结构损伤会引起气动参数变化,进而影响系统的静稳定性和控制精度。针对具有多输入的非线性飞机模型,利用带有二阶命令滤波器的自适应反步控制方法在线估计飞机气动参数,补偿结构损伤导致的气动参数变化对控制系统的影响,以实现容错飞行控制功能;引入的命令滤波器可以避免反步控制中复杂的求导运算。从理论上分析证明了带有二阶命令滤波器的自适应反步控制的闭环系统稳定性,并给出了控制跟踪误差的理论上界和二阶命令滤波器频率参数选取的下界。通过一个大型客机垂直尾翼脱落场景的仿真实验,验证了所提容错控制方法的有效性。     

Aerospace launch vehicle control: an intelligent adaptive approach

A theoretical analysis of on-line autonomous intelligent adaptive tracking controller based on emotional learning model in mammalians brain (BELBIC) for aerospace launch vehicle is presented. The control algorithm is provided with some sensory inputs and reward signal, subsequently it autonomously seeks the proper control signal to be executed by actuators, thus eliminating tracking error without pre-knowledge of the plant dynamics. The algorithm is very robust and fast in adaptation with dynamical change in the plant, due to its on-line learning ability. Development and application of this algorithm for an aerospace launch vehicle during atmospheric flight in an experimental setting is presented to illustrate the performance of the control algorithm.     

直连式三体绳系卫星姿态鲁棒最优跟踪控制

 针对存在参数不确定性以及外部有界干扰的直连式三体旋转绳系卫星系统姿态跟踪控制问题,提出了一种分布式鲁棒最优控制方法。该方法首先针对单体绳系卫星姿态模型,在不考虑参数不确定性和干扰的条件下,应用Hamilton-Jacobi-Bellman方程设计了最优控制器;接着,考虑到实际系统存在参数不确定性和干扰,采用自适应与鲁棒误差积分方法在线学习参数不确定性和有界干扰,与最优控制器结合设计了鲁棒最优控制器,使闭环系统满足了性能指标达到最小的要求,并应用Lyapunov稳定性定理证明了其闭环系统的渐近稳定性。进一步考虑到绳系卫星系统的运动同步性,将单体绳系卫星姿态控制器设计扩展至直连式三体绳系卫星姿态系统,设计了分布式鲁棒最优控制器。最后在MATLAB/Simulink平台上进行了仿真,验证了方法的可行性与有效性,表明其具有潜在的应用前景。     

考虑执行机构饱和的迎角跟踪控制

设计提出了 1种针对高光谱图像分类任务的 3D-MSCNN模型。在 PCA降维的基础上,利用 3D空谱特征提取网络和 2D多尺度特征提取网络实现高光谱图像特征提取,充分发挥高光谱图像空谱信息价值,增强对不同尺度地表覆盖的表达能力。最后,利用 Softmax分类损失函数实现高光谱图像分类任务。实验结果表明,本文算法在 In. dian Pines和 Pavia University数据集上都取得了较好的分类效果。与 CD-CNN、3D-CNN、SS-Net和 HybirdSN等方法相比,本文算法能够有效提升总体精度、平均精度和 Kappa系数等客观评价指标。     

基于跟踪微分器的高超声速飞行器Backstepping控制

针对存在模型参数不确定和外部干扰的高超声速飞行器（HFV）跟踪控制问题,提出一种基于Backstepping方法的抗饱和非线性控制器。将飞行器纵向动力学模型分为速度子系统和航迹倾角子系统,然后针对每个子系统单独设计控制器。设计跟踪微分器获得信号的一阶导数,用以估计系统中的不确定干扰项和避免"微分项膨胀"问题。控制器设计过程考虑了控制量发生饱和的情况。基于Lyapunov理论证明了闭环系统信号的稳定性。与传统高超声速飞行器Backstepping方法相比,所设计的控制器采用待跟踪状态与理想控制指令之间的实际误差作为反馈量,放宽了对系统干扰项的限制,提高了控制器对控制增益变化的适应性,进而提高了闭环系统的跟踪控制性能。对比仿真结果验证了所设计方法的有效性。     

航天器有限时间自适应姿态跟踪容错控制

非刚体航天器存在时变的惯量、执行器完全失效或衰退故障以及外界干扰的情况,提出一种有限时间自适应姿态跟踪容错控制方法。首先,基于有限时间理论和自适应方法,设计惯量不确定性自适应估计项和外界干扰参数自适应估计项进行系统补偿,克服惯量不确定性和抑制外界干扰;然后,基于容错控制和双幂次方法,设计一种自适应有限时间姿态跟踪容错控制算法,并且利用Lyapunov稳定性理论证明所提算法能够保证航天器姿态跟踪系统实际有限时间稳定;最后,对仿真结果进行验证。结果表明:所提有限时间姿态跟踪容错控制方法是有效的。     

GPS/INS组合导航系统自适应滤波算法与仿真研究

随着组合导航系统应用环境的日趋复杂，给噪声统计特性的准确描述带来困难，这将造成Kalman滤波器不稳定甚至发散。首先对目前解决此问题常用的自适应滤波方法进行了总结和分析，在此基础上，给出了基于滤波收敛性判据，结合Sage-Husa自适应滤波和强跟踪Kalman滤波的改进自适应滤波算法。最后以GPS／INS组合导航系统为例进行了计算机仿真，结果表明：该算法可有效抑制滤波发散，具有较大范围的自适应能力。     

基于自适应动态规划的运载火箭智能姿态容错控制

针对运作火箭主动段发动机摆动执行机构故障下的姿态控制问题,结合自适应动态规划（ADP）方法设计了一种智能容错控制策略。该智能容错控制器主要包括2部分：容错稳定控制部分和优化补偿部分。容错稳定控制部分利用自适应和滑模变结构控制设计,主要维持执行机构故障下姿态控制系统的稳定,保证姿态跟踪误差的有限时间收敛;优化补偿部分采用执行-评价结构,利用ADP的在线学习优势,根据姿态系统的跟踪误差（尤其是系统故障、强干扰导致的跟踪偏差）,设计ADP算法产生补偿控制来进一步优化姿态控制系统的跟踪性能。仿真验证表明,即使存在外部干扰和执行机构故障的情况下,所提方法仍能保证系统稳定且精确跟踪指令信号。