Flatness-based control in successive loops for dual-arm robotic manipulators

控制理论(社会学) 机械臂 平坦度(宇宙学) 机械手 对偶(语法数字) 控制工程 操纵器(设备) 计算机科学 工程类 控制(管理) 物理 人工智能 宇宙学 量子力学 文学类 艺术
作者
Gerasimos Rigatos,Masoud Abbaszadeh,Zouari Farouk
出处
期刊:Journal of Vibration and Control [SAGE Publishing]
卷期号:32 (3-4): 488-507 被引量:2
标识
DOI:10.1177/10775463241286550
摘要

Dual-arm robotic manipulators are used in industry and for assisting humans since they enable dexterous handling of objects and more agile and secure execution of pick-and-place, grasping and lifting, or assembling tasks. In this article, a multi-loop flatness-based controller is proposed for the dynamic model of a dual-arm robot. In the considered application, the dual-arm robotic system has to lift and transfer an object under synchronized motion of its two end-effectors while it has also to compensate for the grasping forces between the two end-effectors and the object. The dynamic model of this robotic system is formulated while it is proven that the state-space description of the robot’s dynamics is differentially flat. The control problem for this robotic system is solved with the use of a flatness-based control approach which is implemented in successive loops. To apply the multi-loop flatness-based control method, the state-space model of the dual-arm robotic manipulator is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the preceding i -th subsystem become virtual control inputs for the subsequent ( i + 1)-th subsystem. In turn, real control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The multi-loop flatness-based control approach is an exact nonlinear control scheme which (i) does not use approximate linearization of the dynamic model of the controlled system and (ii) does not need changes of state variables and complicated state-space model transformations.
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