| 禹鑫燚,史栓武,魏岩,欧林林.基于BLF的变阻抗约束人机交互控制[J].高技术通讯(中文),2024,34(11):1189~1199 |
| 基于BLF的变阻抗约束人机交互控制 |
| Variable impedance control based on barrier Lyapunov function for constrained human-robot interaction |
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| DOI： |
| 中文关键词: 人机交互(HRI)； 变阻抗控制； 模型预测控制； 障碍李雅普诺夫函数（BLF）； 神经网络(NN) |
| 英文关键词: human-robot interaction(HRI), variable impedance control, model predictive control, barrier Lyapunov function(BLF), neural network (NN) |
| 基金项目: |
| 作者 | 单位 | | 禹鑫燚 | （浙江工业大学信息工程学院杭州 310023） | | 史栓武 | | | 魏岩 | | | 欧林林 | |
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| 摘要点击次数: 18 |
| 全文下载次数: 36 |
| 中文摘要: |
| 阻抗控制是一种广泛用于人机交互(HRI)的柔顺控制方法，它调节了机器人的跟踪精度和与环境接触的柔顺性。然而，传统的阻抗控制受限于阻抗参数恒定，很难适用于复杂的人机交互任务。为了解决人机交互中领导者与跟随者角色切换问题，本文提出了一种基于障碍李雅普诺夫函数（BLF）的变阻抗约束人机交互控制方法。首先，建立一种新的阻抗模型，将阻抗参数作为新的阻抗系统控制输入，同时将具有阻抗约束的变阻抗控制器设计问题转换成为一个可以求解的二次规划问题。其次，设计了一个基于BLF的自适应神经网络约束控制器来解决跟踪控制问题，同时该控制器保证了误差信号位于约束空间内，其中径向基神经网络用来补偿机器人动力学的不确定性同时保证了跟踪性能，闭环系统的稳定性通过李雅普诺夫稳定性定理进行了证明。最后，通过2个仿真案例验证了所提方法的有效性。 |
| 英文摘要: |
| Impedance control is a compliant control method widely used in human-robot interaction (HRI). It regulates the tracking accuracy of the robot and the contact compliance with the environment. However, fixed impedance parameters limit the applicability of impedance control in complex HRI tasks. In order to realize the role switching between leader and follower in HRI, a variable impedance control approach is proposed based on barrier Lyapunov function (BLF) for constrained HRI. Firstly, A novel impedance model is established and the impedance parameters are designed as the control input of the new impedance system. The design problem of variable impedance controller with impedance constraints is transformed into a quadratic programming problem. Secondly, BLF-based adaptive neural network constrained controller is designed to solve the tracking control problem. At the same time, the controller ensures that the error signal within the constrained domain. The radial basis neural networks (NNs) are adopted to handle uncertainty of the robot dynamics and guarantee tracking performance. The stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, the effectiveness of the proposed method is verified by two simulation cases. |
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