Variable impedance control improves compliance and robustness in robot-environment interaction through variation of the desired stiffness and the desired damping. This paper proposes neural approximation-based variable impedance controllers for robots in robot-environment interaction. Constraints on variable impedance parameters are given to ensure the exponential stability of the desired first- and second-order variable impedance dynamics. Adaptive neural network controllers are proposed to ensure the achievement of the desired first- and second-order variable impedance dynamics through convergence of variable impedance errors. In the neural networks, deadzone modifications are utilized to enhance robustness by turning off adaptation when auxiliary tracking errors enter the constructed small neighbourhoods of zero. The proposed variable impedance control methods in this paper guarantee the stability and achievement of the desired variable impedance dynamics. Theoretical analysis and simulation results validate the effectiveness of the proposed variable impedance control methods.

中文翻译：

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基于神经逼近的机器人自适应可变阻抗控制

可变阻抗控制通过改变所需刚度和所需阻尼来提高机器人与环境交互的顺应性和鲁棒性。本文为机器人与环境交互中的机器人提出了基于神经近似的可变阻抗控制器。给出了对可变阻抗参数的约束，以确保所需的一阶和二阶可变阻抗动态的指数稳定性。提出了自适应神经网络控制器，以通过可变阻抗误差的收敛来确保实现所需的一阶和二阶可变阻抗动态。在神经网络中，当辅助跟踪误差进入构建的零邻域时，死区修改被用来通过关闭自适应来增强鲁棒性。本文提出的可变阻抗控制方法保证了所需可变阻抗动态的稳定性和实现。理论分析和仿真结果验证了所提出的可变阻抗控制方法的有效性。