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Trajectory Tracking Control for Flexible-Joint Robot Based on Extended Kalman Filter and PD Control

机译:基于扩展卡尔曼滤波和局部放电控制的柔性关节机器人轨迹跟踪控制

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摘要

The robot arm with flexible joint has good environmental adaptability and human robot interaction ability. However, the controller for such robot mostly relies on data acquisition of multiple sensors, which is greatly disturbed by external factors, resulting in a decrease in control precision. Aiming at the control problem of the robot arm with flexible joint under the condition of incomplete state feedback, this paper proposes a control method based on closed-loop PD (Proportional-Derivative) controller and EKF (Extended Kalman Filter) state observer. Firstly, the state equation of the control system is established according to the non-linear dynamic model of the robot system. Then, a state prediction observer based on EKF is designed. The state of the motor is used to estimate the output state, and this method reduces the number of sensors and external interference. The Lyapunov method is used to analyze the stability of the system. Finally, the proposed control algorithm is applied to the trajectory control of the flexible robot according to the stability conditions, and compared with the PD control algorithm based on sensor data acquisition under the same experimental conditions, and the PD controller based on sensor data acquisition under the same test conditions. The experimental data of comparison experiments show that the proposed control algorithm is effective and has excellent trajectory tracking performance.
机译:具有柔性关节的机器人手臂具有良好的环境适应性和人机交互能力。然而,用于这种机器人的控制器主要依赖于多个传感器的数据采集,这极大地受到外部因素的干扰,从而导致控制精度的降低。针对状态反馈不完全的柔性关节机器人手臂的控制问题,提出了一种基于闭环PD(比例微分)控制器和EKF(扩展卡尔曼滤波器)状态观测器的控制方法。首先,根据机器人系统的非线性动力学模型,建立了控制系统的状态方程。然后,设计了基于EKF的状态预测观测器。电动机的状态用于估计输出状态,该方法减少了传感器的数量和外部干扰。 Lyapunov方法用于分析系统的稳定性。最后,将所提出的控制算法根据稳定性条件应用于柔性机器人的轨迹控制,并与相同实验条件下基于传感器数据采集的PD控制算法以及基于传感器数据采集的PD控制器进行了比较。相同的测试条件。对比实验的实验数据表明,所提出的控制算法是有效的,具有良好的轨迹跟踪性能。

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