Kang Yu, Wang Tao, Li Pengfei, Xu Zhenyi, Zhao Yun-Bo
IEEE Trans Cybern. 2023 Sep;53(9):5572-5584. doi: 10.1109/TCYB.2022.3159343. Epub 2023 Aug 17.
This article investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function. An open-loop prediction scheme to avoid periodic transmission is designed for the states in the terminal set. As a result, the number of triggering and transmission instants can be reduced significantly. Furthermore, the proposed constraint tightening approach solves the problem of the state constraint satisfaction, which is quite challenging due to the external disturbances and the mutual influences caused by dynamical coupling. Simulations are conducted at last to validate the effectiveness of the proposed algorithm.
本文研究了受状态和控制输入约束的扰动耦合非线性系统的事件触发分布式模型预测控制（DMPC）。提出了一种新颖的复合事件触发DMPC策略，包括复合触发条件和新的约束收紧方法。在这种事件触发策略中，以并行方式检查两个与稳定性相关的条件，这放宽了对李雅普诺夫函数递减的要求。针对终端集中的状态设计了一种避免周期性传输的开环预测方案。结果，可以显著减少触发和传输时刻的数量。此外，所提出的约束收紧方法解决了状态约束满足问题，由于外部干扰和动态耦合引起的相互影响，这一问题颇具挑战性。最后进行了仿真以验证所提算法的有效性。
