Neural Networks Enhanced Adaptive Admittance Control of Optimized Robot–Environment Interaction
提出一种自适应导纳控制方法，利用神经网络和观测器使机器人在未知环境中调节交互力矩并跟踪轨迹，通过仿真验证了有效性。
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.