Lattice piecewise affine approximation of explicit linear model predictive control

Lattice piecewise affine approximation of explicit linear model predictive control
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显式线性模型预测控制的格分段仿射逼近

DOI:
10.1109/cdc45484.2021.9683051
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发表时间:
2021-12
期刊:
2021 60th IEEE Conference on Decision and Control (CDC)
影响因子:
--
通讯作者:
Jun Xu
Jun Xu
中科院分区:
其他
文献类型:
--
作者:
Jun Xu

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在本文中,提出了显式线性模型预测控制(MPC)的晶格分段仿射(PWA)。训练数据由状态样本和相应的仿射控制定律,基于晶格PWA近似。所提出的近似值与包含样品点作为内部点的独特阶(UO)区域的显式MPC控制法相同,因此在许多区域中类似于明确的MPC控制法。通过简化晶格PWA近似中的术语和文字,存储要求和在线计算复杂性都大大降低。通过模拟示例测试了所提出的近似策略的性能,结果表明,晶状体PWA近似非常接近显式MPC控制法。
In this paper, the lattice piecewise affine (PWA) approximation of explicit linear model predictive control (MPC) is proposed. The training data consists of the state samples and corresponding affine control laws, based on which the lattice PWA approximation is constructed. The proposed approximation is identical to the explicit MPC control law in unique-order (UO) regions containing the sample points as interior points, thus resemble the explicit MPC control law in a large number of regions. Through simplifying the terms and literals in the lattice PWA approximation, both the storage requirement and online computation complexity are largely decreased. The performance of the proposed approximation strategy is tested through a simulation example, and the result shows that with a moderate number of sample points, the lattice PWA approximation is very close to the explicit MPC control law.
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