论文标题
用于多步骤顺序模型预测控制的球体解码算法
A Sphere Decoding Algorithm for Multistep Sequential Model Predictive Control
论文作者
论文摘要
本文研究了两个模型预测控制概念，顺序模型预测控制和远程模型模型的预测性控制。为了获得顺序模型预测控制，优化问题分为两个子问题：第一个总结了所有控制目标，这些控制目标是线性依赖于系统输入的。顺序模型预测控制通常需要为第一个子问题获得多个解决方案。因此，由于有限控制集模型的预测控制功率电子的混合性质，因此在本文中提出了特殊的球体解码器。第二个子问题由所有这些控制目标组成，这些控制目标非线性地取决于系统输入，并通过详尽的搜索解决。该方法的有效性通过在不同情况下的数值模拟在三级中性点夹住的永久磁体同步发电机风力涡轮机系统上通过数值模拟进行了验证，并与其他巨体模型预测控制方法进行了比较
This paper investigates the combination of two model predictive control concepts, sequential model predictive control and long-horizon model predictive control for power electronics. To achieve sequential model predictive control, the optimization problem is split into two subproblems: The first one summarizes all control goals which linearly depend on the system inputs. Sequential model predictive control generally requires to obtain more than one solution for the first subproblem. Due to the mixed-integer nature of finite control set model predictive control power electronics a special sphere decoder is therefore proposed within the paper. The second subproblem consists of all those control goals which depend nonlinearly on the system inputs and is solved by an exhaustive search. The effectiveness of the proposed method is validated via numerical simulations at different scenarios on a three-level neutral point clamped permanent magnet synchronous generator wind turbine system and compared to otherlong-horizon model predictive control methods
