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Experimental Investigation on Adaptive Robust Controller Designs Applied to Constrained Manipulators

机译：约束机械臂自适应鲁棒控制器设计的实验研究

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摘要

In this paper, two interlaced studies are presented. The first is directed to the design and construction of a dynamic 3D force/moment sensor. The device is applied to provide a feedback signal of forces and moments exerted by the robotic end-effector. This development has become an alternative solution to the existing multi-axis load cell based on static force and moment sensors. The second one shows an experimental investigation on the performance of four different adaptive nonlinear ℋ∞ control methods applied to a constrained manipulator subject to uncertainties in the model and external disturbances. Coordinated position and force control is evaluated. Adaptive procedures are based on neural networks and fuzzy systems applied in two different modeling strategies. The first modeling strategy requires a well-known nominal model for the robot, so that the intelligent systems are applied only to estimate the effects of uncertainties, unmodeled dynamics and external disturbances. The second strategy considers that the robot model is completely unknown and, therefore, intelligent systems are used to estimate these dynamics. A comparative study is conducted based on experimental implementations performed with an actual planar manipulator and with the dynamic force sensor developed for this purpose.
机译：在本文中，提出了两个交错的研究。首先是针对动态3D力/力矩传感器的设计和构造。该设备用于提供机器人末端执行器施加的力和力矩的反馈信号。这种发展已成为基于静态力和力矩传感器的现有多轴称重传感器的替代解决方案。第二个实验显示了对受模型不确定性和外部干扰影响的约束机械手的四种不同的自适应非线性ℋ∞控制方法的性能的实验研究。评估位置和力的协调控制。自适应规程基于应用于两种不同建模策略的神经网络和模糊系统。第一种建模策略需要为机器人使用众所周知的标称模型，因此智能系统仅用于估计不确定性，未建模的动力学和外部干扰的影响。第二种策略认为机器人模型是完全未知的，因此，使用智能系统来估计这些动力学。基于使用实际平面操纵器和为此目的开发的动态力传感器进行的实验实施，进行了比较研究。

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