无反作用力矩空间机器人轨迹跟踪控制

针对目前关节驱动方式的不足,提出一种采用无反作用力矩驱动方式的空间机器人设计概念。系统平台与各节机械臂上均安装一组金字塔构型的控制力矩陀螺（CMGs）作为力矩执行机构,各臂间由自由球铰连接。采用Rodriguez参数描述平台姿态和机械臂角位移,利用Kane方程建立系统动力学模型。在此基础上,设计了渐近稳定的轨迹跟踪控制律,使得平台姿态和机械臂角位移跟踪期望运动轨迹;并设计了CMGs操纵律,使之准确输出期望控制力矩。此外,研究了机械臂工作空间到关节空间的轨迹规划算法,使得所设计的控制律也可应用于工作空间的轨迹跟踪控制。由三关节系统的仿真结果,验证了无反作用力矩设计概念的可行性和轨迹规划算法的有效性。     

Optimal Earth’s reentry disposal of the Galileo constellation

Nowadays there is international consensus that space activities must be managed to minimize debris generation and risk. The paper presents a method for the end-of-life (EoL) disposal of spacecraft in Medium Earth Orbit (MEO). The problem is formulated as a multiobjective optimisation one, which is solved with an evolutionary algorithm. An impulsive manoeuvre is optimised to reenter the spacecraft in Earth’s atmosphere within 100?years. Pareto optimal solutions are obtained using the manoeuvre $\mathrm{\Delta }v$ and the time-to-reentry as objective functions to be minimised. To explore at the best the search space a semi-analytical orbit propagator, which can propagate an orbit for 100?years in few seconds, is adopted. An in-depth analysis of the results is carried out to understand the conditions leading to a fast reentry with minimum propellant. For this aim a new way of representing the disposal solutions is introduced. With a single 2D plot we are able to fully describe the time evolution of all the relevant orbital parameters as well as identify the conditions that enables the eccentricity build-up. The EoL disposal of the Galileo constellation is used as test case.     

PDE solution to UAV/UGV trajectory planning problem by spatio-temporal estimation during wildfires

Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) have been used in research and development community due to their strong potential in high-risk missions. One of the most important civilian implementations of UAV/UGV cooperative path planning is delivering medical or emergency supplies during disasters such as wildfires, the focus of this paper. However, wildfires themselves pose risk to the UAVs/UGVs and their paths should be planned to avert the risk as well as complete the mission. In this paper, wildfire growth is simulated using a coupled Partial Differential Equation (PDE) model, widely used in literature for modeling wildfires, in a grid environment with added process and measurement noise. Using principles of Proper Orthogonal Decomposition (POD), and with an appropriate choice of decomposition modes, a low-dimensional equivalent fire growth model is obtained for the deployment of the space–time Kalman Filtering (KF) paradigm for estimation of wildfires using simulated data. The KF paradigm is then used to estimate and predict the propagation of wildfire based on local data obtained from a camera mounted on the UAV. This information is then used to obtain a safe path for the UGV that needs to travel from an initial location to the final position while the UAV’s path is planned to gather information on wildfire. Path planning of both UAV and UGV is carried out using a PDE based method that allows incorporation of threats due to wildfire and other obstacles in the form of risk function. The results from numerical simulation are presented to validate the proposed estimation and path planning methods.     

Direct dynamic-simulation approach to trajectory optimization

This paper proposes a new direct method for an efficient trajectory optimization using the point that the dynamics of a deterministic system are uniquely determined by initial states and controls imposed over the time horizon of interest. To effectively implement this concept, the Hermite spline is adopted to interpolate the continuous controls and the system dynamics are integrated with corresponding control parameters in prior. As a result, the optimal control problem can be transcribed into a nonlinear programming problem which has no dynamic equality constraints and no intermediate states in its design variables. In addition, the paper proposes an efficient recursive Jacobian estimation technique and introduces a Jacobian transformation matrix to straightforwardly handle the general state constraints. Important properties of the present method are thoroughly investigated through its applications to the trajectory optimization for a soft lunar landing from a parking orbit, including the detailed analyses for the de-orbiting phase. The computed results are compared with those using the pseudo-spectral method to demonstrate an extreme outperformance of the proposed method in the aerospace applications over the traditional direct method.     

地球-火星之间的往返轨迹全局一体化优化设计

针对多因素影响的地球-火星之间的星际往返轨迹优化设计问题,基于多体动力学运动模型,采用静态参数+动态优化的组合算法,进行了全局一体化优化设计,得到最优往返飞行方案,包括探测器从地球出发到火星的最优4-D飞行轨迹、从火星返回地球的最优4-D轨迹,分别离开地球与火星的时间、与星体的相对速度差等。最优飞行方案达到了在火星上探测时间足够长,往返飞行时间最短,所耗发动机燃料最少等目的。该研究结果对未来我国星际(如地球-各行星之间)最优往返飞行有一定的参考价值。     

混合推进最省燃料轨道设计方法

使用混合推进方式设计地-月圆型限制性三体模型下的最省燃料转移轨道。将化学发动机以及电推进发动机的燃料消耗总和作为目标函数进行优化,推导一阶必要条件和雅可比矩阵。选择从近地圆轨道出发到达地-月L1附近Halo轨道的转移轨道为例测试上述方法。仿真结果表明,相比发射脉冲固定的情况,混合推进方式进一步降低了燃料消耗,而且给出了飞行时间和燃料消耗不同的组合方式,给予任务设计更大的灵活性。     

航天员舱外相对航天器运动轨迹摄动研究

用轨道根数描述载人航天器运动,在其轨道坐标系(LVLH)中,建立了含地球J2项引力和大气阻力摄动加速度的航天员质心相对航天器的运动模型。在参考轨道存在小偏心率时,对基于圆参考轨道假设推导的航天器编队飞行的线性时变系统状态矩阵进行了修正。仿真结果表明:对偏心率为0.002的航天器轨道,修正后模型所得航天员相对轨迹摄动量在离舱300 s后达到0.8%,是修正前模型的5倍,但计算量增加不超过5%,更适于航天员舱外飞行和近距离编队飞行建模。     

异常数据识别的搜索方法

研究弹道跟踪系统的异常数据识别问题,导出了一种基于三角形和四边形面积的异常数据识别的搜索方法。理论分析、仿真计算和实测数据处理均表明,本搜索方法对异常数据的识别有较好的效果,可应用于数据的适时和事后处理。     

运载火箭推力故障不确定性下轨迹可达包络分析

针对运载火箭突发推力故障且故障存在不确定性的轨迹设计问题,提出了一种基于状态转移张量法(STT)的故障火箭轨迹可达包络分析方法。首先,建立了推力故障火箭的运动模型,通过构建故障火箭轨迹重规划问题,基于序列二次规划法获得了不考虑故障不确定性的重规划轨迹; 其次,基于线性协方差对非线性系统的不确定性传播进行建模,并对模型进行线性化,在此基础上提出了基于SST的运载火箭轨迹可达包络分析方法; 最后,通过与多次蒙特卡洛仿真对比验证所提方法在运载火箭轨迹可达包络定量分析中的有效性。     

能量最优的航天器连续动态避障轨迹规划

针对传统脉冲避障算法在航天器轨迹规划应用中存在对瞬时推力依赖性强且燃料消耗量大的问题,提出能量最优的连续动态避障算法.该算法首先基于线性相对运动方程与有限时间的能量最优模型,建立了相对运动能量最优模型,同时验证了模型最优性;其次将动态障碍物的y向运动误差偏移与正态分布概率引入避碰安全距离模型,修正了追踪航天器动态避障的...