Abstract A vehicle-manipulator system (VMS) is a class of mobile robots characterised by their ability to carry or be a robotic arm and therefore also manipulate objects. The VMS class includes vehicles with a robotic manipulator, free-floating space robots, aerial manipulators and underwater vehicle-manipulator systems (UVMSs). All of these systems need a kinematic controller to solve the kinematic redundancy of the VMS and a dynamic controller to follow the reference given by the kinematic controller. In this paper, we propose a combined kinematic and dynamic control approach for VMSs. The approach uses the singularity-robust multiple task-priority (SRMTP) framework to generate a velocity reference combined with a dynamic velocity controller based on a robust sliding mode controller (SMC). Any SMC can be used as long as it can make the velocity vector converge to the velocity reference vector in finite time. This novel approach allows us to analyse the stability properties of the kinematic and dynamic subsystems together in the presence of model uncertainty. We show that the multiple set-point regulation tasks will converge asymptotically to zero without the strict requirement that the velocities are perfectly controlled. This novel approach thus avoids the assumption of perfect dynamic control that is common in kinematic stability analyses for robot manipulators. We present two examples of SMCs that can make the velocity vector converge to the velocity reference vector in finite time. We also demonstrate the applicability of the proposed approach through a simulation study of an articulated intervention-AUV (AIAUV), which is a type of UVMS, by conducting three simultaneous tasks. The results show that both SMC algorithms can make all the regulation tasks converge to their respective set-points. In the simulation study, we also include the results from two standard control methods, a proportional-integral-derivative (PID) controller and a feedback linearisation controller, and we use two different AIAUVs to illustrate the advantages and robustness achieved from using SMC.

中文翻译：

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车辆-机械手系统的组合运动学和动态控制

摘要 车辆-机械手系统（VMS）是一类移动机器人，其特点是能够携带或成为机械臂，因此也能操纵物体。VMS 类包括带有机器人操纵器的车辆、自由漂浮的空间机器人、空中操纵器和水下车辆操纵器系统 (UVMS)。所有这些系统都需要一个运动控制器来解决 VMS 的运动冗余和一个动态控制器来遵循运动控制器给出的参考。在本文中，我们为 VMS 提出了一种结合运动学和动态控制的方法。该方法使用奇异性稳健的多任务优先级 (SRMTP) 框架来生成速度参考，并结合基于稳健滑模控制器 (SMC) 的动态速度控制器。只要能使速度矢量在有限时间内收敛到速度参考矢量，任何 SMC 都可以使用。这种新颖的方法使我们能够在存在模型不确定性的情况下一起分析运动学和动力学子系统的稳定性属性。我们表明，多个设定点调节任务将渐近收敛到零，而没有严格要求完美控制速度。因此，这种新颖的方法避免了在机器人操纵器的运动稳定性分析中常见的完美动态控制的假设。我们展示了两个 SMC 示例，它们可以使速度矢量在有限时间内收敛到速度参考矢量。我们还通过对铰接式干预 AUV (AIAUV) 的模拟研究证明了所提出方法的适用性，这是一种 UVMS，通过同时执行三个任务。结果表明，两种 SMC 算法都可以使所有调节任务收敛到各自的设定点。在仿真研究中，我们还包括了两种标准控制方法的结果，即比例积分微分 (PID) 控制器和反馈线性化控制器，我们使用两种不同的 AIAUV 来说明使用 SMC 的优势和鲁棒性。