论文标题
通过高斯流程的基于学习的自触发控制的提升方法
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes
论文作者
论文摘要
本文研究了网络控制系统（NCS）的自触发控制的设计，其中植物的动力学是未知的。为了应对自触发控制的性质，在这种性质中，状态测量是通过距离传输到控制器的，我们建议将连续的时间动力学提升到新型动力学模型，通过将活动相间时间作为额外的输入，然后通过高斯流程（GP）回归来学习升起的模型。此外，我们提出了一种基于学习的方法，在这种方法中，通过最小化成本函数来学习一个自触发的控制器，以便将样本间的行为考虑在内。通过采用提升方法，我们可以利用基于梯度的策略更新作为一种有效的方法来优化控制和通信策略。最后，我们总结了整体算法并提供了数值模拟，以说明所提出方法的有效性。
This paper investigates the design of self-triggered control for networked control systems (NCS), where the dynamics of the plant is unknown apriori. To deal with the nature of the self-triggered control, in which state measurements are transmitted to the controller a-periodically, we propose to lift the continuous-time dynamics to a novel dynamical model by taking an inter-event time as an additional input, and then, the lifted model is learned by the Gaussian processes (GP) regression. Moreover, we propose a learning-based approach, in which a self-triggered controller is learned by minimizing a cost function, such that it can take inter-sample behavior into account. By employing the lifting approach, we can utilize a gradient-based policy update as an efficient method to optimize both control and communication policies. Finally, we summarize the overall algorithm and provide a numerical simulation to illustrate the effectiveness of the proposed approach.
