IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4551-4561. doi: 10.1109/TNNLS.2021.3057958. Epub 2022 Aug 31.
In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with time-varying dynamics is defined as a linear system. A critic learning method is used to obtain the desired admittance parameters based on the cost function composed of interaction force and trajectory tracking without the knowledge of the environmental dynamics. To deal with dynamic uncertainties in the control system, a neural-network (NN)-based adaptive controller with a dynamic learning framework is developed to guarantee the trajectory tracking performance. Experiments are conducted and the results have verified the effectiveness of the proposed method.
本文为机器人与时变环境交互开发了一种自适应导纳控制方案。导纳控制用于实现机器人与环境的柔顺物理交互，而具有时变动力学的不确定环境被定义为线性系统。采用评价学习方法，基于由交互力和轨迹跟踪组成的成本函数，在不了解环境动力学的情况下获得期望的导纳参数。为了解决控制系统中的动态不确定性，开发了一种基于神经网络（NN）的自适应控制器，并采用动态学习框架，以保证轨迹跟踪性能。进行了实验，结果验证了所提出方法的有效性。
