论文标题
基于模型的非线性周期性事件触发的控制，用于带有采样数据预测的连续时间系统
Model-Based Nonlinear Periodic Event-Triggered Control for Continuous-Time Systems with Sampled-Data Prediction
论文作者
论文摘要
在本文中，我们提出了一种基于模型的定期事件触发的控制机制，用于非线性连续时间网络控制系统。在执行器中使用对系统行为的采样数据预测，以减少所需通信的数量，同时保持用户定义的性能水平。该预测基于非线性系统动力学的可能不准确的离散化，并且可以在简单的硬件上实现。然而，为定期事件触发的控制（PETC）机制提供了渐近稳定性和用户定义的性能水平的保证，而降低了所需的状态信息传输量取决于预测的质量。我们还讨论了如何实施预测。用数值示例说明了提出的PETC机制的性能。本文是[1]的接受版本，还包含主要结果的证明。
In this paper, we present a model-based periodic event-triggered control mechanism for nonlinear continuous-time Networked Control Systems. A sampled-data prediction of the system behavior is used at the actuator to reduce the amount of required communication while maintaining a user-defined performance level. This prediction is based on a possibly inaccurate discretization of the nonlinear system dynamics and can be implemented on simple hardware. Nevertheless, guarantees for asymptotic stability and a user-defined performance level are given for the periodic event-triggered control (PETC) mechanism, whilst the reduction of the required amount of transmissions of state information depends on the quality of the prediction. We discuss furthermore how the prediction can be implemented. The performance of the proposed PETC mechanism is illustrated with a numerical example. This paper is the accepted version of [1], containing also the proofs of the main results.
