Self‐triggered predictive control of nonlinear systems using approximation model
Self‐triggered predictive control of nonlinear systems using approximation model
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DOI：
10.1002/rnc.6322
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发表时间：
2022-07
影响因子：
3.9
通讯作者：
Lixing Yang;Samuel A. Dauchert;Xiaofeng Wang
中科院分区：
文献类型：
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作者：
Lixing Yang;Samuel A. Dauchert;Xiaofeng Wang
本文研究了具有测量噪声和外源干扰的连续时间非线性系统的离散时间模型预测控制。我们考虑离散时间有限视界最优控制问题（FHOCP）的协同设计和相关的自触发方案，该方案调度采样状态和计算FHOCP的时间瞬间。自触发调度的状态依赖特性不仅可以根据系统状态动态调整采样周期，还可以对FHOCP中使用的模型进行动态离散化，如果设计得当，可以降低FHOCP的复杂性。结果表明，只要调度方案与逼近模型相匹配，使得预测状态与实际状态之间的一步逼近误差小于与代价函数相关的阈值，系统就可以具有一致的最终有界性。这些结果可以应用于大多数现有的固定或时变采样率的模型近似方法。
This article studies discrete‐time model predictive control of continuous‐time nonlinear systems with measurement noises and exogenous disturbances. We consider the co‐design of discrete‐time finite horizon optimal control problem (FHOCP) and the associated self‐triggering schemes that schedule the time instants for sampling the state and computing the FHOCP. The state‐dependent nature of the self‐triggered scheduling can not only dynamically adjust the inter‐sampling period according to the system status, but dynamically discretize the model used in the FHOCP as well, in order to reduce the complexity of the FHOCP if designed appropriately. It is shown that the system can be stabilized with uniform ultimate boundedness, as long as the scheduling scheme matches the approximation model such that the one‐step approximation error between the predicted state and the actual state is below a threshold related to the cost function. These results can be applied to most existing model approximation methods with either fixed or time‐varying sampling rates.