多自由度动量交换技术研究的新进展

航天器三轴姿态控制常用的多飞轮控制方案中,反作用飞轮的单自由度动量交换属性使系统的功能重复部件和冗余件数目增多,严重影响了姿控系统的重量、体积、功耗及成本;由于原理和结构的限制,传统多自由度动量交换技术无法充分发挥其“多自由度”的固有优势,而新型多自由度动量交换技术的研究与发展则为这一优势的发挥提供了空间。本文综述了国内外多自由度动量交换技术研究的新进展,分析了其特点、关键技术和研究方向,并展望其应用前景。     

航天器总装多余物控制方法探讨

带有多余的航天产品存在重大的质量事故隐患。文章对航天器总装中的多余物进行了分类,从而分析多余物产生的机理有4大来源:总装工艺过程、零组件及人员带入、贮存过程、有害气体。介绍了多余物的危害,提出了系统控制多余物的思路和多余物常用检查及排除的方法。     

Solar sailing in realistic mode,based on a locally-optimal control laws

Ballistic design of solar sailing missions in the solar system is composed of defining the design parameters, the control programs, and the trajectories that provide performance goals of a flight. The use of a solar sail spacecraft imposes specific restrictions on mission parameters that include the degradation limit on the flight duration, the maximum temperature of solar sail's surface, the minimum distance from the Sun, the maximum angular velocity of the spacecraft's rotation and others.Many authors considered the impact of these restrictions on the design of the mission separately, but they used a sophisticated method of finding the exact optimal motion control or applied the most straightforward laws of motion control. This paper uses local-optimal control laws at the complete mathematical models of motion and functioning of solar sail spacecraft to describe a technique of designing interplanetary missions. The described method avoids the need to obtain an accurate optimal solution to the control problem and does not cause significant computational difficulties.     

Solar sails for perturbation relief: Application to asteroids

A major cause of spacecraft orbital variation comes from natural perturbations, which, in close proximity of a body, are dominated by its non-spherical nature. For small bodies, such as asteroids, these effects can be considerable, given their uneven (and uncertain) mass distribution. Solar sail technology is proposed to reduce or eliminate the net secular effects of the irregular gravity field on the orbit. Initially, a sensitivity analysis will be carried out on the system which will show high sensitivity to changes in initial conditions. This presents a challenge for optimisation methods which require an initial guess of the solution. As such, the Genetic Algorithm (GA) is proposed as the preferred optimisation method as this requires no initial guess from the user. A multi-objective optimisation is performed which aims to achieve a periodic orbit whilst also minimising the effort required by the sail to do so. Given the system sensitivity, the control law for one orbit is not necessarily applicable for any subsequent orbit. Therefore, a new method of updating the control law for subsequent orbits is presented, based on linearisation and use of a Control Transition Matrix (CTM). The techniques will later find application in a multiple asteroid rendezvous mission with a solar sail as the primary propulsion system.     

Propulsionless planar phasing of multiple satellites using deep reinforcement learning

This work creates a framework for solving highly non-linear satellite formation control problems by using model-free policy optimisation deep reinforcement learning (DRL) methods. This work considers, believed to be for the first time, DRL methods, such as advantage actor-critic method (A2C) and proximal policy optimisation (PPO), to solve the example satellite formation problem of propellantless planar phasing of multiple satellites. Three degree-of-freedom simulations, including a novel surrogate propagation model, are used to train the deep reinforcement learning agents. During training, the agents actuated their motion through cross-sectional area changes which altered the environmental accelerations acting on them. The DRL framework designed in this work successfully coordinated three spacecraft to achieve a propellantless planar phasing manoeuvre. This work has created a DRL framework that can be used to solve complex satellite formation flying problems, such as planar phasing of multiple satellites and in doing so provides key insights into achieving optimal and robust formation control using reinforcement learning.     

Backstepping sliding-mode control of stratospheric airships using disturbance-observer

In the presence of unknown disturbances and model parameter uncertainties, this paper develop a nonlinear backstepping sliding-mode controller (BSMC) for trajectory tracking control of a stratospheric airship using a disturbance-observer (DO). Compared with the conventional sliding mode surface (SMS) constructed by a linear combination of the errors, the new SMS manifold is selected as the last back-step error to improve independence of the adjustment of the controller gains. Furthermore, a nonlinear disturbance-observer is designed to process unknown disturbance inputs and improve the BSMC performances. The closed-loop system of trajectory tracking control plant is proved to be globally asymptotically stable by using Lyapunov theory. By comparing with traditional backstepping control and SMC design, the results obtained demonstrate the capacity of the airship to execute a realistic trajectory tracking mission, even in the presence of unknown disturbances, and aerodynamic coefficient uncertainties.     

基于变量集结预测控制的四旋翼无人机控制器设计

针对四旋翼无人机路径跟踪问题,设计了一种基于变量集结预测控制的控制器。首先以四旋翼无人机状态空间模型为基础建立预测模型;接着设计控制器时采用分段集结的策略把控制量集结成三段优化序列以减少优化计算量,并降低优化保守性,为了进一步加强系统稳定性,控制器引入终端代价函数和终端约束;最后用四旋翼无人机的动力学模型作为被控对象进行仿真验证,结果表明：该控制器能使四旋翼无人机在三轴方向均能实现一个良好的路径跟踪效果。与传统方法相比,这种基于变量集结的预测控制策略能够减小在线优化量,更适合四旋翼无人机飞控芯片的应用。     

PMSM电流预测控制参数误差量化分析方法

交流永磁同步电机（PMSM）电流环控制性能是制约交流伺服系统性能的关键。电流预测控制拥有更快的动态响应、更低的电流谐波和优良的转矩响应,但该算法依赖精确的电机模型,参数失配会引发稳态电流误差,无法输出额定转矩,进而导致电机转矩输出效率降低。根据永磁同步电机电流预测模型,详细分析了d、q轴电流静差产生的原因,以及电流预测控制对电机参数误差的敏感性,提出了一种参数误差量化分析方法。该方法引入了电机参数偏差因子,量化描述了电机电感、磁链参数误差与电流静差之间的制约关系。通过仿真分析,验证了所提方法的合理性,为高性能永磁伺服电流预测数字控制技术打下了良好基础。     

可变速发电机对多机电力系统的稳定性提高的影响

阐述了可变速发电机(ASG)对多机电力系统的稳定性提高有着明显的效果。为了利用电力系统模拟仿真器进行实时仿真，开发了基于个人计算机(PC)的可变速发电机模组。通过将Matlab／Simulink环境下建立的ASG三相瞬时值模型装到PC里的数字处理器(DSP)板上运行，来实现更为详细的实时仿真。通过DSP板上的AD／DA转换接口，对基于PC的ASG模组与电力系统模拟仿真器等实施了物理连接，进行了多机电力系统的实时仿真．所有的仿真结果均显示，引入可变速发电机后，多机电力系统的整体稳定性有了大幅度的提高．     

涡轮基组合循环发动机分布式控制系统通信网络混合拓扑结构优化方法

涡轮机组合循环(Turbine based combined cycle,TBCC)发动机控制系统通信网络拓扑结构是其分布式控制系统方案设计的重要部分,优化网络拓扑结构可提高发动机推重比和控制系统可靠性。本文基于智能优化算法提出TBCC分布式控制系统网络拓扑结构优化方法。基于图论建立TBCC几何模型和网格模型,以重量和可靠性为优化性能指标,同时考虑发动机表面高温区域以及控制节点的工作可靠性,分别采用粒子群算法和遗传算法优化星形结构中智能中央节点位置、中央节点的环形拓扑结构,获得星形-环形混合拓扑结构。仿真实例表明,基于本文方法优化所得的混合拓扑结构相较于星形集中式控制结构,系统重量降低了51.9%。