基于扰动观测器的机械臂自适应反演滑模控制

机械臂的动力学模型通常包含一定的结构不确定性，并受到外界未知干扰的影响。针对现有模型的不确定性特点，提出了一种基于非线性扰动观测器的自适应反演滑模控制方法，解决机械臂的轨迹跟踪控制问题。对于外界干扰，利用非线性扰动观测器进行观测补偿，无需上界先验知识；对于结构不确定性，引入反演滑模控制，同时设计自适应律，保证闭环系统的稳定性并增强系统的动态适应性。仿真结果证明，所提出的方法可以有效克服系统不确定性，降低控制输入信号的抖振，最终实现期望轨迹的快速精确跟踪。

Disturbance Observer-based Adaptive Backstepping Sliding Mode Control for Manipulators

The dynamic model of the manipulator usually contains certain structural uncertainties and is always influenced by unknown disturbance. An adaptive backstepping sliding mode control method based on nonlinear disturbance observer is proposed to solve the problem of trajectory tracking control for the manipulator in this paper. The nonlinear disturbance observer is used to observe the external disturbance without the prior knowledge of the upper bound. For structural uncertainties, backstepping sliding mode control with the adaptive law is introduced to ensure the stability of the closed-loop system and enhance the system dynamic adaptability. The simulation results show that the proposed method can effectively overcome the uncertainty of the system, reduce the buffing of the input signal and finally realize the fast and accurate tracking of the desired trajectory.