Aerial manipulators expand the application scenarios of manipulators into the air. To complete various operations, the contact force between the aerial manipulator and the target must be precisely controlled. In this study, we first established the mathematical models of the multirotor and the manipulator separately. Their mutual influence is regarded as each other’s disturbance, and the overall linkage mechanism is established through analysis. Then, a robust sliding mode control strategy is developed for accurate trajectory tracking. The controller is derived from Lyapunov theory, which can ensure the stability of the closed-loop system. To compensate for the effect of system uncertainty, an adaptive radial basis function neural network is devised to approximate the part of the controller containing the model information. In addition, an impedance controller is designed to convert force control into position control to make the manipulator contact with the target compliantly. Finally, the simulation and experimental results indicate that the proposed method can guarantee the accuracy of the contact force and has good robustness.

中文翻译：

---

基于自适应神经网络鲁棒控制的空中机械臂柔顺接触力控制

空中机械手将机械手的应用场景拓展到了空中。为了完成各种操作，必须精确控制空中机械臂与目标之间的接触力。在本研究中，我们首先分别建立了多旋翼和机械臂的数学模型。将它们的相互影响视为彼此的干扰，通过分析建立整体的联动机制。然后，开发了鲁棒滑模控制策略以实现精确的轨迹跟踪。该控制器源自Lyapunov理论，可以保证闭环系统的稳定性。为了补偿系统不确定性的影响，设计了自适应径向基函数神经网络来近似控制器中包含模型信息的部分。此外，设计了阻抗控制器，将力控制转化为位置控制，使机械臂与目标柔顺接触。最后，仿真和实验结果表明，该方法能够保证接触力的准确性，并且具有良好的鲁棒性。