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Robust Admittance Control of Optimized Robot–Environment Interaction Using Reference Adaptation

Guangzhu Peng, C. L. Philip Chen, Chenguang Yang

2022IEEE Transactions on Neural Networks and Learning Systems36 citationsDOI

Abstract

In this article, a robust control scheme is proposed for robots to achieve an optimal performance in the process of interacting with external forces from environments. The environmental dynamics are defined as a linear model, and the interaction performance is evaluated by a defined cost function, which is composed of trajectory errors and force regulation. Based on admittance control, the reference adaptation method is used to minimize the cost function and achieve the optimal interaction performance. To make the trajectory tracking controller robust to the unknown disturbance of internal system dynamics, an auxiliary system is defined and the approximation optimal controller is designed. Experiments on the Baxter robot are conducted to verify the effectiveness of the proposed method.

Topics & Concepts

Control theory (sociology)TrajectoryAdmittanceController (irrigation)RobotProcess (computing)Robust controlComputer scienceTracking (education)Function (biology)Control engineeringRobustness (evolution)Internal modelAdaptation (eye)Optimal controlControl systemAdaptive controlScheme (mathematics)Control (management)EngineeringLinear systemTracking errorReference modelFeedback controllerMathematicsSystem dynamicsTransfer functionRobot kinematicsTeleoperation and Haptic SystemsRobot Manipulation and LearningAdaptive Control of Nonlinear Systems