论文标题
耗时的模仿学习，以鲁棒性动态输出反馈
Dissipative Imitation Learning for Robust Dynamic Output Feedback
论文作者
论文摘要
强大的模仿学习试图在确保稳定性的同时模仿专家控制器行为，但是当前的方法需要准确的植物模型。在这里，针对具有线性动态输出反馈的稳定模仿学习是为了稳定建模较差的植物。开环输入输出属性用于表征不确定的植物，并通过控制器的开环QSR-Dissipativity属性来实施动态控制器的反馈矩阵。模仿学习方法应用于具有参数不确定性的两个系统。
Robust imitation learning seeks to mimic expert controller behavior while ensuring stability, but current methods require accurate plant models. Here, robust imitation learning is addressed for stabilizing poorly modeled plants with linear dynamic output feedback. Open-loop input-output properties are used to characterize an uncertain plant, and the feedback matrix of the dynamic controller is learned while enforcing stability through the controller's open-loop QSR-dissipativity properties. The imitation learning method is applied to two systems with parametric uncertainty.
