A parametric branch and bound approach to suboptimal explicit hybrid MPC
A parametric branch and bound approach to suboptimal explicit hybrid MPC
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DOI：
10.1016/j.automatica.2013.10.004
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Daniel Axehill;T. Besselmann;D. Raimondo;M. Morari
中科院分区：
文献类型：
--
作者：
Daniel Axehill;T. Besselmann;D. Raimondo;M. Morari
本文提出了一个参数分支定界算法，用于计算参数混合整数二次规划和参数混合整数线性规划的最优解和次优解。该算法返回一个最优或次优的参数解决方案与用户所要求的次优水平。一个有趣的应用所提出的参数分支和界限的过程是次优显式MPC的混合动力系统，其中引入的用户定义的次优容限减少了存储要求和在线计算工作量，甚至使计算的次优MPC控制器的情况下，最优MPC控制器的计算将是棘手的。此外，系统的稳定性在闭环次优控制器可以保证先验。
In this article we present a parametric branch and bound algorithm for computation of optimal and suboptimal solutions to parametric mixed-integer quadratic programs and parametric mixed-integer linear programs. The algorithm returns an optimal or suboptimal parametric solution with the level of suboptimality requested by the user. An interesting application of the proposed parametric branch and bound procedure is suboptimal explicit MPC for hybrid systems, where the introduced user-defined suboptimality tolerance reduces the storage requirements and the online computational effort, or even enables the computation of a suboptimal MPC controller in cases where the computation of the optimal MPC controller would be intractable. Moreover, stability of the system in closed loop with the suboptimal controller can be guaranteed a priori.