Robust self-triggered MPC for constrained linear systems
Robust self-triggered MPC for constrained linear systems
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DOI：
10.1109/ecc.2014.6862397
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
F. D. Brunner;W. M. Heemels;F. Allgöwer
中科院分区：
文献类型：
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作者：
F. D. Brunner;W. M. Heemels;F. Allgöwer
在本文中，我们为线性系统提供了一种强大的自触发模型预测控制算法，该系统具有添加剂有界干扰和对输入和状态的硬性约束。在自触发的控制中，在每次采样时，直到下一个采样瞬间是根据系统的当前状态在线计算的。目的是达到较低的平均采样率，从而最大程度地减少控制系统中的通信，并可能按照稀疏控制应用程序所要求的减少控制更新的数量。自然而有意的是，我们的方法会导致长时间以开环的方式控制植物的时间。特别是对于不稳定的植物或大型干扰，这需要考虑到控制法的设计中的干扰特征，以防止闭环系统中的约束违规。我们使用Tube模型预测控制中提出的约束收紧方法来保证稳健的约束满意度。显示自触发控制器可在闭环系统的状态空间中稳定稳定的不变设置。
In this paper we propose a robust self-triggered model predictive control algorithm for linear systems with additive bounded disturbances and hard constraints on the inputs and state. In self-triggered control, at every sampling instant the time until the next sampling instant is computed online based on the current state of the system. The goal is to achieve a low average sampling rate, thereby minimizing communication in the control system and possibly reducing the number of control updates as is required in sparse control applications. Naturally, and intentionally, our approach leads to long spans of time in which the plant is controlled in an open-loop fashion. Especially for unstable plants or large disturbances this necessitates taking into account the disturbance characteristics in the design of the control law in order to prevent constraint violation in the closed-loop system. We use constraint tightening methods as proposed in Tube Model Predictive Control to guarantee robust constraint satisfaction. The self-triggered controller is shown to stabilize a robust invariant set in the state space for the closed-loop system.