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Min-max MPC using a tractable QP problem
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DOI：10.1016/j.automatica.2006.10.006.png)
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摘要
En 中文
Min-max model predictive controllers (MMMPC) suffer from a great computational burden that is often circumvented by using approximate solutions or upper bounds of the worst possible case of a performance index. This paper proposes a computationally efficient MMMPC control strategy in which a close approximation of the solution of the min-max problem is computed using a quadratic programming problem. The overall computational burden is much lower than that of the min-max problem and the resulting control is shown to have a guaranteed stability. A simulation example is given in the paper. (C) 2007 Elsevier Ltd. All rights reserved.
Keyword:
robust control
uncertain systems
min-max
model predictive control
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期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
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引用论文
Constrained min-max predictive control:: Modifications of the objective function leading to polynomial complexity约束最小-最大预测控制:: 导致多项式复杂性的目标函数的修改
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