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A warm-started homogeneous and self-dual interior-point method for linear economic model predictive control

机译:线性经济模型预测控制的热启动齐次自对偶内点法

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In this paper, we present a warm-started homogenous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control (MPC) of linear systems. To exploit the structure in the optimization problems, our algorithm utilizes a Riccati iteration procedure which is adapted to the non-standard system solved in homogenous and self-dual IPMs, and specifically tailored to economic MPC. Fast convergence is further achieved by means of a recent warm-starting strategy for homogenous and self-dual IPMs that has not previously been applied to MPC. We implement our algorithm in MATLAB and its performance is analyzed based on a smart grid power management case study. Closed loop simulations show that 1) our algorithm is significantly faster than state-of-the-art IPMs based on sparse linear algebra routines, and 2) warm-starting reduces the number of iterations by approximately 15-35%.
机译:在本文中,我们针对线性系统的经济模型预测控制(MPC)中出现的线性程序,提出了一种热启动的均质和自对偶内点法(IPM)。为了在优化问题中利用结构,我们的算法采用了Riccati迭代程序,该程序适用于在同质和自对偶IPM中解决的非标准系统,并且专门针对经济型MPC进行了量身定制。通过最近针对同质和自对偶IPM的最新热启动策略,进一步实现了快速收敛,该策略以前尚未应用于MPC。我们在MATLAB中实现我们的算法,并基于智能电网电源管理案例研究分析了其性能。闭环仿真表明,1)我们的算法比基于稀疏线性代数例程的最新IPM显着快,并且2)热启动将迭代次数减少了大约15-35%。

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