带扩展卡尔曼滤波的柔性关节机器人虚拟分解控制

Virtual decomposition control for flexible-joint robot manipulators with extended Kalman filtering

中文关键词  机器人  柔性关节  虚拟分解控制  扩展卡尔曼滤波  轨迹跟踪

英文关键词  robot manipulator  flexible joint  virtual decomposition control  extended Kalman filter  trajectory tracking

中文摘要

      针对柔性关节机器人在非完全状态反馈条件下的轨迹跟踪控制问题, 本文提出一种基于虚拟分解控制 (virtual decomposition control, VDC)理论和扩展卡尔曼滤波(extended Kalman filtering, EKF) 观测的控制方法. 首先, 考虑模型参数的不确定性和外界扰动因素, 分别设计刚性连杆子系统和柔性关节子系统的虚拟分解控制律. 然后, 为突破现有VDC方法依赖于全状态反馈测量的局限, 设计一种基于EKF的间接状态观测器, 实现了仅需电机侧位置 和速度测量而不需连杆侧任何状态信息测量的闭环控制. 此外, 结合虚拟稳定和李雅普诺夫稳定理论给出了严格的 系统稳定性证明. 最后, 实例对比仿真验证了所提出控制算法的有效性, 且相比于基于传统拉格朗日整体动力学的 典型算法, 具有更优的轨迹跟踪性能.

英文摘要

      A general control approach based on virtual decomposition control (VDC) and extended Kalman filtering (EKF) is proposed in this paper, to realize trajectory tracking control for flexible-joint robot manipulators via non-full-state feedback. First, considering parametric uncertainties in robot model and external disturbances, virtual-decomposed control law for rigid-link subsystems and flexible-joint subsystems are designed, respectively. Then, to break through the limitations that the existing VDC method always needs full-state feedback, an EKF based indirect state observer is presented, to make the closed-loop control with only position and velocity measurements from the motor side of each joint, and without any state measurements from the link side. In addition, strict stability analysis of the system is given according to the theory of virtual stability and Lyapunov stability. Finally, simulation example verifies the effectiveness of the proposed approach, and comparison results show its superior tracking performance than the other typical controller based on the traditional Lagrange integrated dynamic model.