In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted.

中文翻译：

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优化机器人__环境交互的神经网络增强自适应导纳控制


在本文中，开发了一种用于机器人与未知环境交互的导纳适应方法。与之交互的环境被建模为线性系统。在存在未知动态环境的情况下，采用机器人关节空间中的观测器来估计相互作用扭矩，并采用导纳控制来调节相互作用点处的机器人行为。采用径向基函数的自适应神经控制器来保证轨迹跟踪。通过导纳自适应来最小化定义扭矩调节和轨迹跟踪的交互性能的成本函数。为了验证所提出的方法，对机器人操纵器进行了仿真研究。