Adaptive reactionless control strategy via the PSO-ELM algorithm for free-floating space robots during manipulation
Adaptive reactionless control strategy via the PSO-ELM algorithm for free-floating space robots during manipulation
复制标题
通过 PSO-ELM 算法为自由漂浮空间机器人在操纵过程中提供自适应无反应控制策略
DOI：
10.1007/s11071-017-3947-6
复制
发表时间：
2017
影响因子：
5.6
通讯作者：
崔晶
中科院分区：
文献类型：
--
作者：
楚中毅;马也;崔晶
自由漂浮空间机器人的运行对航天器本体的姿态控制提出了很大的挑战，这是由于系统中存在强动力学耦合以及被捕获物体附加的未知特性造成的。传统的无反作用运动规划和控制方法需要精确的系统动力学知识和理想的路径跟踪性能，这是不可能实现的现实。针对这一问题，提出了一种自由漂浮空间机器人在操纵未知物体时的自适应无反应路径规划与控制集成策略。具体而言，采用滑动窗口递归最小二乘（SW-RLS）算法，通过对系统动量方程的时变矩阵形式的未知属性进行辨识和补偿，构造自适应无反应路径规划。同时，在设计PD控制器减小误差的基础上，提出了一种基于粒子群优化-极限学习机（PSO-ELM）算法的鲁棒自适应控制策略，以跟踪规划路径的动态变化。在计算效率的基础上，通过PSO-ELM的自适应控制器旨在智能地学习和补偿未知属性;然后，使用鲁棒控制器来抑制模型不确定性。所提出的策略的显着特点是，它既不需要一个准确的系统模型，也不需要任何信息的未知属性。最重要的是，该设计可以动态实现无反应路径跟踪性能。最后，以自由漂浮空间机器人捕获未知目标为例进行了仿真，并与现有方法进行了比较，验证了所提方法的有效性。
The operation of free-floating space robots causes significant challenges to the attitude control of a spacecraft body, resulting from the strong dynamic coupling existing in the system as well as the appending unknown property from a captured object. Conventional reactionless motion planning and control methods require the precise knowledge of system dynamics with ideal path tracking performance, which are impossible to achieve in reality. To solve this problem, an adaptive reactionless path planning and control integrated strategy is proposed for free-floating space robots while manipulating unknown objects. For details, a SW-RLS (Slide-windowed Recursive Least Square) algorithm is employed to construct the adaptive reactionless path planning by identifying and compensating the unknown property in the form of a time-variable matrix for the momentum equation of the system. Simultaneously, a robust adaptive control strategy via the PSO-ELM (Particle Swarm Optimization-Extreme Learning Machine) algorithm is proposed to track the dynamic change in the planned path, based on the foundation of a designed PD controller for reducing the error. With computational efficiency, an adaptive controller via PSO-ELM aims at learning and compensating for the unknown property intelligently; then, a robust controller is used to reject the model uncertainties. The notable feature of the proposed strategy is that it requires neither an accurate system model nor any information about the unknown property. Most importantly, the design can dynamically realize reactionless path tracking performance. Finally, the simulations of a free-floating space robot capturing an unknown object and comparisons with existing methods are presented to demonstrate the effectiveness of the proposed scheme.