School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Sensors (Basel). 2022 Dec 12;22(24):9740. doi: 10.3390/s22249740.
This paper presents an impedance learning-based adaptive control strategy for series elastic actuator (SEA)-driven compliant robots without the measurement of the robot-environment interaction force. The adaptive controller is designed based on the command filter-based adaptive backstepping approach, where a command filter is used to decrease computational complexity and avoid the requirement of high derivatives of the robot position. In the controller, environmental impedance profiles and robotic parameter uncertainties are estimated using adaptive learning laws. Through a Lyapunov-based theoretical analysis, the tracking error and estimation errors are proven to be semiglobally uniformly ultimately bounded. The control effectiveness is illustrated through simulations on a compliant robot arm.
本文提出了一种基于阻抗学习的自适应控制策略，用于串联弹性执行器（SEA）驱动的柔顺机器人，无需测量机器人与环境的相互作用力。自适应控制器基于基于命令滤波的自适应反演方法设计，其中使用命令滤波器来降低计算复杂度并避免对机器人位置的高导数的要求。在控制器中，使用自适应学习律来估计环境阻抗轮廓和机器人参数不确定性。通过基于 Lyapunov 的理论分析，证明了跟踪误差和估计误差是半全局一致最终有界的。通过对柔顺机器人臂的仿真，说明了控制效果。
