Adaptive robust synchronized control for cooperative robotic manipulators with uncertain base coordinate system. (July 2022)

• Record Type: Journal Article
• Authors: Zhai, Anbang; Wang, Jin; Zhang, Haiyun; Lu, Guodong; Li, Howard
• Is Part Of: ISA transactions. Volume 126(2022)
• Page Start: 134
• Page End: 143
• Publication Date: 2022-07
• Subjects: Cooperative robotic manipulators -- Synchronized control -- Uncertain base coordinate system -- Robust sliding mode control -- Adaptive neural network; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.07.036
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In this paper, cooperative robotic manipulators under uncertain base coordinate are investigated. The coordinate uncertainties result in biases of cooperative robotic dynamics, which involve horizontal and vertical translational errors in the task space and rotational errors in the joint space. To the best of our knowledge, uncertainties in the base coordinate system of cooperative robotic manipulators have drawn little attention in existing literature. To solve this problem, this paper presents an adaptive robust controller for the synchronized control of two cooperative robotic manipulators. An adaptive neural network associated with base coordinate parameter adaption law is proposed to estimate the cooperative system parameters given unknown system dynamics and base coordinate uncertainties. A synchronization-factor-based robust slide mode controller is then derived to stabilize the target position and internal force between the cooperative manipulators. Mathematical proof and numerical experiments under various conditions are conducted. The results demonstrate the satisfactory and effective convergences of both the cooperative robotic trajectory and internal force despite of uncertainties in the base coordinate system. Highlights: The base uncertainties including both translation and rotation errors are modelled. A base coordinate parameter adaption law is proposed. Adaptive neural network is proposed to estimate cooperative system uncertainties. A virtual synchronization-factor-based robust sliding controller is presented. Position and internal force tracking errors quickly converge to zero in four cases.