Adaptive Visual Control for Robotic Manipulators with Consideration of Rigid-Body Dynamics and Joint-Motor Dynamics

Zhitian Chen, Weijian Wen () and Weijun Yang
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Zhitian Chen: School of Automation, Guangdong University of Technologyy, Guangzhou 510006, China
Weijian Wen: School of Intelligent Manufacturing, Guangzhou City Polytechnic, Guangzhou 510405, China
Weijun Yang: School of Intelligent Manufacturing, Guangzhou City Polytechnic, Guangzhou 510405, China

Mathematics, 2024, vol. 12, issue 15, 1-21

Abstract: A novel cascade visual control scheme is proposed to tailor for electrically driven robotic manipulators that operate under kinematic and dynamic uncertainties, utilizing an uncalibrated stationary camera. The proposed control approach incorporates adaptive weight radial basis function neural networks (RBFNNs) to learn the behaviors of the uncertain dynamics of the robot and the joint actuators. The controllers are designed to nullify the approximation error and mitigate unknown disturbances through an integrated robust adaptive mechanism. A major advantage of the proposed approach is that prior knowledge of the dynamics of the robotic manipulator and its actuator is no longer required. The controller autonomously assimilates the robot and actuator dynamics online, thereby obviating the need for fussy regression matrix derivation and advance dynamic measurement to establish the adaptive dynamic parameter update algorithm. The proposed scheme ensures closed-loop system stability, bounded system states, and the convergence of tracking errors to zero. Simulation results, employing a PUMA manipulator as a testbed, substantiate the viability of the proposed control policy.

Keywords: adaptive control; uncalibrated system; actuator dynamics; visual servoing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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