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A Reduced Dantzig-Wolfe Decomposition for a Suboptimal Linear MPC

机译:用于次优线性mpC的Dantzig-Wolfe降阶分解

摘要

Linear Model Predictive Control (MPC) is an efficient control technique that repeatedly solves online constrained linear programs. In this work we propose an economic linear MPC strategy for operation of energy systems consisting of multiple and independent power units. These systems cooperate to meet the supply of power demand by minimizing production costs. The control problem can be formulated as a linear program with block-angular structure. To speed-up the solution of the optimization control problem, we propose a reduced Dantzig-Wolfe decomposition. This decomposition algorithm computes a suboptimal solution to the economic linear MPC control problem and guarantees feasibility and stability. Finally, six scenarios are performed to show the decrease in computation time in comparison with the classic Dantzig-Wolfe algorithm.
机译:线性模型预测控制(MPC)是一种有效的控制技术,可反复求解在线受限线性程序。在这项工作中,我们提出了一种经济的线性MPC策略,用于由多个独立动力单元组成的能源系统的运行。这些系统通过最小化生产成本来配合满足电力需求。可以将控制问题表述为具有块角结构的线性程序。为了加快优化控制问题的解决速度,我们提出了简化的Dantzig-Wolfe分解。该分解算法计算出经济的线性MPC控制问题的次优解,并保证了可行性和稳定性。最后,执行六种情况以显示与经典Dantzig-Wolfe算法相比减少的计算时间。

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