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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Spacecraft actuator alignment determination through null-motion excitation
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Spacecraft actuator alignment determination through null-motion excitation

机译：通过零运动激励确定航天器执行器对准

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Current methods of system identification inject random or sinusoidal signals into the system and obtain feedback to learn or infer system parameters. In general, these methods do not consider the learning convergence property of the signal or its effects upon the overall system. In addition, the richness of a random signal is reduced when passed through the actuator that acts as a low-pass filter. Furthermore, the choice of both random and sinusoidal signals typically do not consider the effect of the motion on the controlling body (e.g., movement of the tool point for a robot manipulator with respect to its joint motion, movement of the entire satellite with respect to its attitude actuators, or movement of the Mars rover with respect to its wheels). A design of experiments is developed here to support reaction wheel assembly parameter identification to satisfy persistence of excitation, thereby learning system parameters without inducing large perturbations to the controllable body (e.g., spacecraft bus). This approach exploits the null-motion solutions of overactuated systems and provides a gradient-based method to identify the direction along the null space to be excited for fast local convergence of the learned parameters. The discussed method hypothesizes that a family of null-motion solutions from the excitation law will cause small but measurable perturbations upon the controllable body. Also, the motion of the actuators has the ability of being of larger amplitude and frequency than would be available structurally for the controllable body states, which makes it valid for some current satellite programs. The outcome of this research is a design of experiments structured for direct nonlinear adaptive control for an overactuated system to determine actuator alignment. This is accomplished using excitation through the null space, combined with a proposed gradient-based method (all assuming known mass and environment properties).
机译：当前的系统识别方法将随机或正弦信号注入系统，并获得反馈以学习或推断系统参数。通常，这些方法不考虑信号的学习收敛性或其对整个系统的影响。另外，当通过用作低通滤波器的致动器时，随机信号的丰富度减小。此外，随机信号和正弦信号的选择通常不考虑运动对控制体的影响（例如，机器人操纵器的工具点相对于其关节运动的运动，整个卫星相对于机器人运动的运动）。姿态致动器或火星漫游车相对于其车轮的运动）。这里开发了一种实验设计，以支持反作用轮组件参数识别，以满足激励的持久性，从而学习系统参数，而不会引起对可控物体（例如，航天器总线）的大扰动。这种方法利用了过激励系统的零运动解，并提供了一种基于梯度的方法来识别沿零空间被激励的方向，以实现学习参数的快速局部收敛。所讨论的方法假设，来自激励定律的一系列零运动解将对可控物体产生较小但可测量的扰动。而且，致动器的运动具有比在结构上可控制的人体状态可利用的振幅和频率更大的能力，这使其对于某些当前的卫星程序有效。这项研究的结果是为过度驱动的系统进行直接非线性自适应控制以确定执行器对准而设计的实验设计。这是通过在零空间中激发，并结合提出的基于梯度的方法（均假设已知的质量和环境属性）来完成的。

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