Safety-Constrained Intelligent Control of a 2-DOF Robotic Manipulator Using Control Barrier Functions
Overview
This paper presents a control architecture that combines a radial basis function neural network compensator with Cartesian tracking and joint-space control barrier functions to improve trajectory tracking while respecting torque, velocity, and position limits under uncertain dynamics. An auxiliary state driven by the difference between requested and applied torques helps correct learning signals and reduce filter intervention effects. The approach is evaluated on a two-link manipulator through constrained regulation, ablation studies, matched uncertainty trials, disturbance tests, and operating-range analyses. Results indicate improved tracking performance and maintained safety constraints despite parameter uncertainties.
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- arXiv Robotics — research abstractsSignalSafety-Constrained Intelligent Control of a 2-DOF Robotic Manipulator Using Control Barrier Functions
This paper presents a control architecture that combines a radial basis function neural network compensator with Cartesian tracking and joint-space control barrier functions to improve trajectory tracking while respecting torque, velocity, and position limits under uncertain dynamics. An auxiliary state driven by the difference between requested and applied torques helps correct learning signals and reduce filter intervention effects. The approach is evaluated on a two-link manipulator through constrained regulation, ablation studies, matched uncertainty trials, disturbance tests, and operating-range analyses. Results indicate improved tracking performance and maintained safety constraints despite parameter uncertainties.
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