论文标题
设计自适应稳定的扩展Kalman过滤器，基于基于Lyapunov的机器人操纵器的控制器
Designing adaptive robust extended Kalman filter based on Lyapunov-based controller for robotics manipulators
论文作者
论文摘要
在本文中，考虑了受恒定有界干扰影响的机器人操纵器的位置和速度估计方法。跟踪控制问题被提出为干扰拒绝问题，所有未知参数和动态不确定性都陷入了干扰。使用自适应鲁棒扩展的Kalman滤波器（AREKF），预计每个接头的运动和速度被预测用于不连续的Lyapunov的控制器结构。错误动力学的参数已通过真实数据离线验证。给出了两种自由度操纵器的计算机仿真结果，通过比较EKF和改进的AREKF的性能，证明了改进的卡尔曼滤波器的功效。尽管表明可以使用建议的控制器来实现准确的轨迹跟踪。
In this paper, a position and velocity estimation method for robotic manipulators which are affected by constant bounded disturbances is considered. The tracking control problem is formulated as a disturbance rejection problem, with all the unknown parameters and dynamic uncertainties lumped into disturbance. Using adaptive robust extended Kalman filter(AREKF) the movement and velocity of each joint is predicted to use in discontinuous Lyapunov-based controller structure. The parameters of the error dynamics have been validated off-line by real data. Computer simulation results given for a two degree of freedom manipulator demonstrate the efficacy of the improved Kalman Filter by comparing the performance of EKF and improved AREKF. Although it is shown that accurate trajectory tracking can be achieved by using the proposed controller.
