基于深度神经网络的双臂无人机机械臂系统自适应RISE控制
Adaptive RISE Control for Dual-Arm Unmanned Aerial Manipulator Systems with Deep Neural Networks
AI总结：
本文针对双臂无人机机械臂系统质心变化、未建模效应和外部扰动问题，提出基于深度神经网络的自适应RISE控制器，并通过Lyapunov分析保证渐近收敛，硬件实验验证了方法的实用性与鲁棒性。
摘要：
由多旋翼无人机（UAV）和机械臂组成的无人机机械臂系统已引起研究者的广泛关注。然而，双臂机械臂的操作带来了动力学挑战，因为系统的质心（CoM）会随机械臂运动而变化，从而可能影响多旋翼无人机。此外，未建模效应、参数不确定性以及外部扰动会显著降低控制性能，导致不可预见的危险。为解决这些问题，本文提出了一种基于深度神经网络（DNN）的非线性自适应RISE（误差符号的鲁棒积分）控制器。第一步是建立双臂空中机械臂的运动学和动力学模型。随后，提出带有DNN前馈项的自适应RISE控制器，以有效应对内部和外部挑战。通过采用Lyapunov技术，严格保证了跟踪误差信号的渐近收敛。值得注意的是，本文是一项开创性工作，首次提出了基于DNN的自适应RISE控制器设计，并附有全面的稳定性分析。为验证所提控制方法的实用性和鲁棒性，进行了多组实际硬件实验。结果证实了所开发方法在处理真实场景中的有效性，从而为双臂空中机械臂系统的性能提供了有价值的见解。
英文摘要：
The unmanned aerial manipulator system, consisting of a multirotor UAV (unmanned aerial vehicle) and a manipulator, has attracted considerable interest from researchers. Nevertheless, the operation of a dual-arm manipulator poses a dynamic challenge, as the CoM (center of mass) of the system changes with manipulator movement, potentially impacting the multirotor UAV. Additionally, unmodeled effects, parameter uncertainties, and external disturbances can significantly degrade control performance, leading to unforeseen dangers. To tackle these issues, this paper proposes a nonlinear adaptive RISE (robust integral of the sign of the error) controller based on DNN (deep neural network). The first step involves establishing the kinematic and dynamic model of the dual-arm aerial manipulator. Subsequently, the adaptive RISE controller is proposed with a DNN feedforward term to effectively address both internal and external challenges. By employing Lyapunov techniques, the asymptotic convergence of the tracking error signals are guaranteed rigorously. Notably, this paper marks a pioneering effort by presenting the first DNN-based adaptive RISE controller design accompanied by a comprehensive stability analysis. To validate the practicality and robustness of the proposed control approach, several groups of actual hardware experiments are conducted. The results confirm the efficacy of the developed methodology in handling real-world scenarios, thereby offering valuable insights into the performance of the dual-arm aerial manipulator system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
