论文标题
通过系统级合成对时间延迟系统的强大模型预测控制
Robust Model Predictive Control of Time-Delay Systems through System Level Synthesis
论文作者
论文摘要
我们为具有状态和控制输入约束的离散时间线性时间删除的系统提供了强大的模型预测控制方法（MPC）。该系统均受多重模型的不确定性和加性干扰的约束。在提出的方法中，优化了随时间变化的反馈控制策略，以确保对闭环系统对所有约束的稳健满意。通过编码延迟状态和输入反馈策略的效果，我们使用系统级合成解决了MPC中强大的最佳控制问题，该系统综合导致凸二次二次程序，该程序共同执行不确定性过度应用和鲁棒控制器的合成。值得注意的是，二次程序中的变量数与延迟范围无关。我们提出的方法的有效性和可扩展性在数值上得到了证明。
We present a robust model predictive control method (MPC) for discrete-time linear time-delayed systems with state and control input constraints. The system is subject to both polytopic model uncertainty and additive disturbances. In the proposed method, a time-varying feedback control policy is optimized such that the robust satisfaction of all constraints for the closed-loop system is guaranteed. By encoding the effects of the delayed states and inputs into the feedback policy, we solve the robust optimal control problem in MPC using System Level Synthesis which results in a convex quadratic program that jointly conducts uncertainty over-approximation and robust controller synthesis. Notably, the number of variables in the quadratic program is independent of the delay horizon. The effectiveness and scalability of our proposed method are demonstrated numerically.
