New formulation of robust MPC by incorporating off-line approach with on-line optimization

New formulation of robust MPC by incorporating off-line approach with on-line optimization
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通过将离线方法与在线优化相结合,提出了鲁棒 MPC 的新公式

DOI:
10.1080/00207720701393328
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发表时间:
2007-06
影响因子:
4.3
通讯作者:
--
中科院分区:
计算机科学4区
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--
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本文讨论具有多面描述的系统的鲁棒模型预测控制 (MPC)。现有的离线鲁棒MPC使得在线计算就像在查找表中搜索状态反馈增益一样简单。我们考虑通过加入自由扰动项来扩大离线 MPC 的吸引力区域。首先,离线构建更大的椭圆体吸引区域,在线解决简单的优化问题。然后,在这个较大的椭球体之外,在线求解标准的稳健 MPC,以便可以将整个吸引区域渲染为一个更大的非椭球体区域。所提出的方法具有离线 MPC 的所有优点,并大大扩大了吸引力区域。给出了仿真例子来验证其有效性。
This article addresses robust model predictive control (MPC) for systems with polytopic description. An existing off-line robust MPC enables the on-line computation as simple as searching a state feedback gain among the look-up table. We consider enlarging the region of attraction of the off-line MPC by incorporating free perturbation items. First, a larger ellipsoidal region of attraction is off-line constructed, in which a simple optimization problem is solved on-line. Then, outside of this larger ellipsoid, a standard robust MPC is solved on-line such that the overall region of attraction can be rendered as a much larger nonellipsoidal one. The proposed approach possesses all the merits of the off-line MPC and greatly enlarges the region of attraction. A simulation example is given to demonstrate the effectiveness.
DOI: 10.1109/acc.2001.946109
发表时间: 2001-08
期刊: Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148)
影响因子: --
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