In order to help the operator perform the human-robot collaboration task and optimize the task per-formance,an adaptive control method based on optimal admittance parameters is proposed.The overall control structure with the inner loop and outer loop is first established.The tasks of the inner loop and outer loop are robot control and task optimization,respectively.Then an inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is proposed,which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator.Subsequently,the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force.The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model.The model includes the unknown dynamics of the operator and the task performance details.For relaxing the requirement of the system model,the integral reinforcement learning is employed to solve the linear quadratic regulator problem.Besides,an auxiliary force is designed to help the operator complete the specific task better.Compared with the traditional control scheme,the security performance and interaction performance of the human-robot collabora-tion system are improved.The effectiveness of the proposed method is verified through two numerical simulations.In addition,a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method.

YU Xinyi;WU Jiaxin;XU Chengjun;LUO Huizhen;OU Linlin

College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China

上海交通大学学报（英文版）

[1]YU Xinyi;WU Jiaxin;XU Chengjun;LUO Huizhen;OU Linlin-.Adaptive Human-Robot Collaboration Control Based on Optimal Admittance Parameters)[J].上海交通大学学报（英文版）,2022(05):589-601

Admittance,securely

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