In order to help the operator perform the human-robot collaboration task and optimize the task performance,
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 collaboration
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 (禹鑫燚), WU Jiaxin (吴加鑫), XU Chengjun (许成军), LUO Huizhen (罗惠珍), OU Linlin∗ (欧林林)
. Adaptive Human-Robot Collaboration Control Based on Optimal Admittance Parameters[J]. Journal of Shanghai Jiaotong University(Science), 2022
, 27(5)
: 589
-601
.
DOI: 10.1007/s12204-022-2460-3
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